Blood flow velocity analysis in cerebral perforating arteries on 7T 2D phase contrast MRI with an open-source software tool (SELMA)

Blood flow velocity analysis in cerebral perforating arteries on 7T 2D phase contrast MRI with an open-source software tool (SELMA) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blood flow velocity analysis in cerebral perforating arteries on 7T 2D phase contrast MRI with an open-source software tool (SELMA) S. D.T. Pham, C. Chatziantoniou, J. T. Vliet, R. J. Tuijl, M. Bulk, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5045336/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Neuroinformatics → Version 1 posted 7 You are reading this latest preprint version Abstract Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland-Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (v mean ) and velocity PI of SELMA was very similar to the original results (v mean : mean difference ± standard deviation: 0.1 ± 0.8 cm/s; velocity PI: mean difference ± standard deviation: 0.01 ± 0.1) despite the slightly higher number of detected vessels in SELMA (N detected : mean difference ± standard deviation: 4 ± 9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69–0.95) and the overall intra-class coefficient for N detected , v mean , and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cerebral blood flow in large intracranial arteries can be quantified using phase contrast magnetic resonance imaging (PC-MRI). This technique estimates the blood flow velocity from the MR signal's phase accrual of moving spins as the velocity of the moving spins (i.e. blood) is proportional to this phase accrual. Recently, with the advantages of the increased sensitivity on high-field 7T MRI, granting higher spatial resolutions with adequate SNR, blood flow velocity of the brain's perforating arteries through a two-dimensional plane can be measured using 2D PC-MRI 1 , 2 . Blood flow velocity measurements can inform on the condition of these arteries, notably also in disease states 3 – 6 . In particular, the blood flow velocity's pulsatility index (PI) is an important indicator of arterial stiffness 7 – 9 . Increased stiffness in the large intracranial arteries has been associated with damage to the brain parenchyma, such as microbleeds 10 , 11 , lacunar infarcts 10 , 12 , and white matter hyperintensities 10 , 13 , 14 , while also being linked to cognitive impairment 7 , 15 and cognitive decline 15 , 16 . In the smaller arteries, information from these blood flow velocity measurements could be especially relevant in cerebral small vessel diseases (cSVDs), an important cause of stroke and dementia 17 . Recently, using 7T MRI, an increased velocity PI in the perforating arteries was found in patients with cSVDs and a lower blood flow velocity was linked to white matter damage increase/progression 18, 19 . Age was also found to be a determinant of increased velocity PI in the perforating arteries of the basal ganglia in both cSVD and general population cohorts 5 , 20 , 21 . For the larger intracranial arteries, several publicly available image processing tools exist to analyze PC-MRI data 22 – 25 . For the smaller arteries, however, there are no processing tools available yet. Reported analyses of PC-MRI data of the smaller arteries were performed with a collection of in-house developed code 2 , 26 , 27 , which has several limitations. First, such code consists of scripts in which the parameters of the algorithms are hard-coded and not accessible as user-defined settings. Second, these scripts, including parameter settings, often evolve with time without explicit version tracking. Also, as part of the developments of the analysis methods, several variants of output parameters were computed in parallel, such as the PI from the mean normalized velocity traces from all arteries and the mean over the PIs of the individual velocity traces. This situation can potentially create ambiguities and unintentionally result in inconsistent application of output parameters in studies. Additionally, it impedes the reproducibility of previous analyses, even more so across centers. Finally, the lack of version-controlled software with a user-friendly user interface hampers the dissemination of the methods to other researchers, which limits the usage of blood flow velocity and PI measurements in the perforating arteries of the basal ganglia and semioval center on a larger scale. To support consistent, repeatable analysis and to promote the usage and possibly further development of the PC-MRI measurements in small arteries, an analysis tool should be established that is accessible (open-source), user-friendly (with a graphical user interface (GUI)), and facilitate repeatable analysis (logging of version and settings with results) of small artery PC-MRI data. This work presents the Small vessEL MArker (SELMA) analysis software as a novel, open-source tool for cerebral small artery flow velocity analysis compatible with data from multiple MRI vendors. SELMA incorporates converged and updated analysis algorithms used in our previous publications 2 , 26 , 28 . To validate the implementation of the algorithm in SELMA, we re-analyzed previously published data 29 and compared the results of SELMA with the original results obtained with a previously published iteration of the algorithm. Additionally, we assessed the inter-rater reliability of SELMA on data from three different 7T MRI vendors by two trained operators. A secondary aim was to assess the performance of measuring perforating artery velocity pulsatility at MRI scanners from different sites and vendors by comparing measurements between sites and testing for age and sex effects. Methods Algorithm The original algorithm used to analyze small artery PC-MRI data as described in previous work 2 , 26 , 28 was developed in MATLAB (version R2015b, the MathWorks, Natick, MA). These MATLAB scripts have been rewritten in Python 3.7 to develop SELMA for analysis of the cerebral perforating arteries at the level of the basal ganglia or the white matter at the semioval center (Fig. 1 ). SELMA was developed to be compatible with all 2D PC-MRI DICOM data, regardless of MRI vendor or field strength. The implementation of the algorithm in SELMA follows that of the original codes 2 , 26 , 28 , but includes several updates and improvements, which will be detailed below. Analysis in SELMA can be started by drawing a region of interest (ROI) on the 2D PC-MRI images using the software interface or loading such mask from the disk. The first step of the analysis is the estimation of the noise level in the phase and magnitude frames, which is computed on a voxel-wise basis using the standard deviation of the complex signal (constructed from the phase and magnitude data) over the cardiac cycle. The root mean square of the standard deviations of the imaginary and complex parts of the constructed complex signal are computed next. A median filter with a user-defined kernel size (default 10 mm) is then applied to the root mean square maps. The temporal signal-to-noise ratio of the magnitude frames (SNR mag ) is computed by dividing the magnitude frames with the median filtered root mean square maps. The phase maps are scaled to velocity maps with the pre-defined velocity encoding (v enc ) of the scan protocol parameters available from the DICOM header. The velocity frames are averaged over the cardiac cycle and median-filtered with a 10 mm kernel. The median filtered velocity map is subtracted from the raw velocity maps at each point in the cardiac cycle to remove the background phase of static tissue and to center the velocity around zero. Next, using SNR mag , the standard deviation of the corrected velocity maps (σ v ) can be computed using the following formula 30 : $$\:{\sigma\:}_{v}=\frac{{v}_{enc}}{\pi\:}\frac{1}{{SNR}_{mag}}$$ The corrected velocity maps are divided by σ v to obtain the temporal signal-to-noise ratio of the velocity frames (SNR v ). Based on the SNR mag and SNR v , voxels in the 2D PC-MRI image can be clustered into several profiles (Table 1 ). For the detection of vessels in the basal ganglia, clusters of voxels with an SNR mag and SNR v above a positive noise threshold (T n ) are detected as potential arteries. Clusters of voxels in the semioval center are identified as arteries only if SNR v is lower than the negative T n , regardless of SNR mag . The sign for SNR v in the semioval center is changed due to the opposite directionality of the blood flow of the perforating arteries at the semioval center compared to the basal ganglia. T n can be changed by the user by changing the significance level in the settings (default 0.05, in which case Tn = 1.96). A new addition in SELMA is the scaling of T n based on the number of acquired heart phases and the temporal resolution of the data. Estimation of the SNR in the magnitude and velocity frames are dependent on the heart rate, which affects the number of acquired heart phases, and on the number of k-space lines acquired per heartbeat and TR of the acquisition, which affects the temporal resolution. After identification of the arteries that exceed T n , operators of SELMA can select the options to automatically filter out arteries whose orientation is far from being perpendicular to the scanning plane and/or to deduplicate arteries that are too close to each other. The blood flow velocity in arteries that are not perpendicular to the scanning plane will be underestimated 2 . SELMA considers the artery's roundness to determine whether the artery can be considered as perpendicular to the scanning plane, at least in first approximation, in which case it should be included in the analysis. An ellipse is fitted to the artery's circumference, and the axes ratio of the ellipse is computed, which discards the artery from further analysis if the axes ratio exceeds a user-set threshold (default 2). Artery detections that are too close to each other might be multiple detections on a single artery oriented parallel to the scanning plane or might be caused by ghosting artefacts. In such cases, SELMA discards all arteries except the one with the highest velocity if detected within a user-set distance (default 1.2 mm) from each other. These options can be applied both to basal ganglia and semioval center focussed PC-MRI scans. Additional options can be selected to erode the outer region of the ROI and/or filter out ghosting artefacts from the scans. Arteries in the outer edges of the ROI in the semioval center are more prone to motion artifacts. Users can specify how many voxels from the edges of the ROI can be eroded (default 80). Ghosting of larger arteries in the phase encoding direction can lead to erroneous artery detections. SELMA incorporates the automatic ghosting censoring method as previously described 26 . First, SELMA identifies the large blood arteries by applying a relative intensity threshold on the magnitude images. Only clusters of voxels in the top magnitude percentile defined by the user (default 0.3%) and larger than a minimum size are included as potential large arteries. Depending on the size of the bright voxel cluster, an exclusion zone will be drawn around the cluster. Undesired detections in these zones will be discarded from further analysis. Ghosting artefacts are a larger issue in the semioval center when automatically segmented ROIs are used compared to the basal ganglia where they can be avoided when manually delineating a ROI. In the options, users can define custom values for the thresholds, voxel sizes of the arteries or length and width of the exclusion zones. Another addition to SELMA is the ability for operators to censor artery detections in the basal ganglia manually. Selecting this option will override the in-plane artery censoring and deduplication steps. SELMA will visualize all detected arteries and guide the user systematically through every artery prompting the user if it should be discarded for analysis. The user can see the proximity of the artery to others in the user interface, and the axes ratio of the artery is provided to aid the user in this process. The user can opt to keep or discard the highlighted artery, and is then guided to the next one. After all detected arteries have been evaluated, SELMA prompts the user to either save their results, or to restart the manual vessel censoring process again from the beginning. All parts of the manual vessel censoring functionality can be directly performed in the GUI. After final artery selection, the number of perforating arteries (N detected ), their mean blood flow velocity (v mean ) and the velocity PI are assessed. The PI is generally defined as \(\:\frac{{v}_{max}-{v}_{min}}{{v}_{mean}}\) , where v max , v min , and v mean , are the maximum, minimum, and mean of the velocity trace over the cardiac cycle in cm/s. We calculated the PI from the averaged velocity trace of all final included arteries, while the individual velocity traces were normalized before averaging (hence, v mean in the PI formula was 1.0 by definition). During the development of SELMA, an approach using the median of the normalized velocity trace was also considered, however during internal testing, we found that the blood flow velocity distribution over all arteries for each time point in the cardiac cycle followed a normal distribution and that using a median estimator had a higher uncertainty than the mean estimator, which led to inflated values of the PI 19 . Thus, we opted to use the mean of the normalized velocity trace to compute the PI. During internal testing, we observed similar group differences in blood flow velocity and velocity PI using the mean versus median normalized velocity trace. User interface Analysis of small vessel 2D PC-MRI data has been made more user friendly with the addition of a GUI in SELMA (Fig. 1 ). 2D PC-MRI scans of either the basal ganglia or the semioval center in DICOM format can be loaded into SELMA via the menu in the top bar. ROIs that were either manually drawn in SELMA or segmented with external software, such as white matter masks, can also be loaded from the disk with this menu. Analysis can be started with the 'Analyse' option in the menu for single scans or, if masks are already provided, entire folders containing scans. The batch analysis option in SELMA allows for automatic analysis of multiple scans without any user input, drastically speeding up the analysis process. Two default voxel clustering settings have been defined, tailored to acquisitions at the basal ganglia and semioval center, respectively. Before the analysis, the actual slice location must be selected in the bottom right-hand corner to ensure that the corresponding predefined voxel clustering settings are applied in SELMA. Custom clustering options can also be selected in this menu for analysis of data that do not fall into the standard profiles for basal ganglia or semioval center scans. The remaining options in the top menu allow for changing the contrast or zoom of the image, which can also be directly manipulated in the user interface using the mouse. In the settings menu, the operator can change several options and thresholds as described in the 'Algorithm' section. SELMA was developed specifically with the goal of an easy to maintain code base. To this end, the GUI and the processing parts of the code are intentionally separated in different classes that can only talk to each other using the Signal-and-Slot paradigm. This should enable future developers to easily understand the code, and make small changes to either the processing algorithms or the GUI without accidentally introducing unwanted behavior elsewhere in the program. Test data The implementation of the analysis algorithm in SELMA was validated with 7T MRI (Achieva 7T, Philips Medical Systems, Best, The Netherlands) data from previous work 29 . These 2D PC-MRI data were acquired with a 32-channel receive head coil (Nova Medical, Wilmington, NC, United States) in 14 patients with coarctation of the aorta and 15 control subjects with no history of cardiovascular disease, neurological disease, or intellectual disability. The 2D PC sequence was planned on a 3D T1-weighted image at the level of the basal ganglia (Fig. 2 ) and was retrospectively gated using a peripheral pulse oximeter for triggering. The following scan parameters were used: 250 × 250 mm 2 field of view; acquired spatial resolution 0.3 × 0.3 × 2.0 mm 3 , reconstructed spatial resolution (through zero-filling in k-space) 0.2 × 0.2 × 2.0 mm 3 ; TR/TE = 28/14.7–15.1 ms; flip angle = 50°; TFE factor = 2 (i.e. number of k-lines acquired per cardiac frame, per heart beat); velocity encoding = 20 cm/s; acquired temporal resolution = 112 ms; 13–15 reconstructed heart phases, depending on the heart rate; SENSE factor = 1.5; scan duration was about 5 minutes for a heart rate of 60 beats/min. Inter-rater reliability was assessed with data from 8 different scanning sites comprising three different MRI vendors: Philips (Achieva 7T, Philips Healthcare, Best, The Netherlands), Siemens (MAGNETOM 7T, Siemens Healthineers, Erlangen, Germany) and General Electric (GE) (Discovery MR950, GE Healthcare, Chicago, Illinois, USA). All MRI data were acquired with a 32-channel receive head coil (Nova Medical, Wilmington, NC, United States), except for one site where a 24-channel receive head coil (Nova Medical, Wilmington, NC, United States) was used. All scanning sites participated in the European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND) 31 . EUFIND comprises researchers from 22 7T MRI sites in Europe with the common aim of identifying opportunities and challenges of 7T MRI for clinical and research applications in neurodegeneration. Each site included healthy elderly subjects aged between 50 and 70 years with an equal gender distribution and attempted to include an equal number of volunteers between 50 and 60 years and between 60 and 70 years to allow for secondary analysis of age and gender effects on the outcome parameters. All institutions performed the scanning under the approval of the institution's local ethical review board, and all subjects provided written informed consent. The 2D PC-MRI sequence was originally developed for the Philips 7T MRI and the protocol was harmonized across all sites with the parameters in Table 2 . Harmonization was limited by the vendor-specific implementation of the 2D PC sequence and parameter ranges. The acquisition slice was planned in the basal ganglia, targeting the perforating lenticulostriate arteries that branch from the circle of Willis. Representative basal ganglia scans per vendor are shown in Fig. 2 . Data analysis The differences in the outcome measures between SELMA and the previously published results on the validation data were quantified on a group level with a Bland-Altman analysis. Inter-rater reliability of the ROIs drawn in SELMA was assessed using the Dice similarity coefficient (DSC) between ROIs drawn by two trained operators. Inter-rater reliability of the outcome measures using manual artery selection was assessed by calculating the Intra-class Correlation Coefficient (ICC) between two operators. An ICC above 0.75 was considered to be an excellent correlation between operators 32 . The coefficient of variation (CV) of all outcome measures and the SNR v of the included arteries averaged over all subjects within each site were used to assess inter-site differences. The CV for all sites within a single vendor was computed and subsequently averaged over all vendors to assess the inter-vendor differences. The relation of age and sex with the outcome measures was assessed using a linear mixed model corrected with site and vendor as random effects. All statistical analyses were performed in MATLAB (version R2021a, the MathWorks, Natick, MA) except for the linear mixed modelling, which was performed in R (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria) with the 'lme4' package. Results Validation of the implementation Table 3 shows the results of the original measurements compared to SELMA on previously published data of 29 participants 24 . In the same data, SELMA had a slightly higher N detected compared to the previous version of the algorithm. The v mean and velocity PI were similar between both approaches at a group level. Bland-Altman plots for the comparison between the original measurements and SELMA are shown in Figure 3. Multi-vendor comparison Data of 60 participants (mean age ± standard deviation: 59±6 years) were included (eight sites, three MRI vendors) for the multi-vendor analysis. Due to vendor-specific implementation of the 2D PC sequence, scan parameters slightly varied between subject, site and vendor. Table 4 provides an overview of the relevant parameters. The N detected , v mean, PI, and SNR v are stratified per vendor in Table 5 and per site in Table 6. The CV for inter-site differences were larger for all measurements than the inter-vendor differences (Table 7). Inter-rater analysis The mean DSC for the manually drawn ROIs by the two operators was 0.91 (range 0.69-0.95) across all 60 participants (Table 4). All ICCs for the measurements exceeded 0.75 except for the velocity PI of vendor 1 and N detected of vendor 2. The ICC of N detected , v mean , and velocity PI for the entire dataset was 0.92, 0.84, and 0.85, respectively (Table 5). Age and gender analyses Age and gender were not significantly associated with N detected , v mean , and velocity PI over the entire population in multivariate models adjusted for site and vendor (Figure 4). Discussion In this study, we developed SELMA, an open-source small cerebral artery analysis tool for 2D PC-MRI data. We have shown that the results using SELMA match well with the results of the original version of our algorithm using published data 29 . Reproducible multi-vendor analysis of 2D PC-MRI data was also possible with SELMA. With SELMA, we obtained a higher N detected than in the previous analysis, which can be explained by the changes that were made in the deduplication steps. Previously, all perforating arteries within a 1.2 mm distance from each other were discarded in the analysis, which led to more false negatives during detection. In SELMA, only the artery with the highest v mean is kept, and all other detections within a 1.2 mm distance are discarded. The increase in N detected had no effect on the average v mean and velocity PI, as these were similar to the original results over the entire group. This supports the original assumption that averaging over all available small arteries gives a representative number for the detected population of arteries 2 . The Limits of Agreement (LoA) for the velocity PI found in the Bland-Altman analysis was similar to the LoA on a group-level for the original measurements on 7T (LoA original versus SELMA: 0.20 vs 0.16) 24 , suggesting that the variability in velocity PI can mostly be explained by thermal or physiological noise during the measurement rather than a bias introduced by different analysis methods. On multi-vendor data, analysis with SELMA showed that the CV in the outcome measures was higher between sites than between vendors. The CV for N detected was higher than the other outcome measures in both comparisons. The high variance in N detected can be explained by various factors causing the sensitivity differences for detecting arteries across sites. Differences in the reconstruction software of the scanners could likely partially explain this variation. The variation of N detected between sites of the same vendor might reflect variation in patient handling by the local MRI technician, such as the usage of padding to limit motion, which was not standardized and may have varied between sites. Also, despite slice planning was standardized via an illustrated instruction, variations between technicians in the execution of this planning cannot be prevented and could effect the number of visible vessels. Another source of variation could be that the protocol could slightly vary between sites as gradient performance or the availability of (commercial) options in the software could vary and the protocol at each site was implemented as close to the requested parameters as possible. All these factors could have contributed to the variance of N detected and these differences in sensitivity illustrate the challenges in harmonizing the 2D PC-MRI sequence across multiple sites and vendors, as shown in the differences in scan parameters between sites and vendors (Table 3 ) and SNR v differences (Table 4 ). In an early attempt by the EUFIND group, the 2D PC-MRI sequence was also attempted to harmonize for the perforating arteries in the semioval center. However, large sensitivity differences between vendors made the implementation of this sequence in these small (≤ 300µm) perforating arteries difficult 31 . These sensitivity differences might also affect the CVs of the outcome measures of the perforating arteries in the basal ganglia. The v mean and velocity PI over the entire dataset were slightly higher than reported for control groups in other studies 18 , 20 , 29 . This can likely be attributed to the high variance in N detected due to the aforementioned sensitivity differences across sites. Smaller arteries with a blood flow velocity just above the noise threshold might not be detected in several sites, which could lead to an overestimation of v mean and velocity PI in these participants. A similar effect has been observed when measuring these perforating arteries at 3T MRI 29 . The mean DSC for the manually drawn ROIs on all scans across all sites and vendors (mean: 0.91; range: 0.69–0.95) was comparable to earlier results on 3T 2D PC-MRI data which was single-vendor only (mean: 0.90; range: 0.85–0.95) 33 . The option to manually censor the results of the automatic artery detection in the basal ganglia was added after the observation that the automatic detection could not always successfully remove in-plane arteries (i.e. arteries not perpendicular to the imaging plane). The lenticulostriate arteries in the basal ganglia are quite tortuous 34 , which can be a challenge for the automatic in-plane artery removal and deduplication steps in SELMA. The arteries in the semioval center are smaller than in the basal ganglia and are censored better with the automatic censoring steps. The operator can freely choose which arteries to include or exclude with manual censoring. The ICCs after manual artery censoring exceeded 0.75 for all outcome measurements over the entire dataset, indicating that despite the differences in acquisition parameters in the entire dataset, manual censoring yields good to excellent inter-rater reliability. The overall ICC for the velocity PI was higher than the reported ICCs for test-retest reliability by Schnerr et al 21 ., suggesting that the SELMA results are probably less dependent on the operator (manual ROI delineation and vessel censoring) than on the measurement noise (scan-to-scan variability). We found no relation between age or gender and the outcome measures when corrected for sites and vendors using a linear mixed model. In literature, velocity PI of the lenticulostriate arteries has been found to be positively associated with age 5 , 20 , 21 , and males had a higher velocity PI in the internal carotid arteries 35 but not in the lenticulostriate arteries 20 compared to females. The lack of an age effect on the velocity PI in our dataset could be explained by both the differences in acquisition parameters and sensitivity between sites and vendors and the relatively small age range of the study participants in our study (mean age: 59; range: 50–70; standard deviation: 6 years) when compared to the other studies in the literature 5 , 20 , 21 . In an additional analysis, we found velocity PI to be associated with N detected in our dataset (Supplementary Table 1). As a result of the differences in acquisition between sites, velocity PI seems to be dependent on N detected , which could also explain the lack of an age effect on the velocity PI. The association between N detected and PI was negative, which suggests that a lower N detected means that only the relatively larger vessels (with accordingly higher PI) are detected. Analysis of cerebral perforating arteries of the basal ganglia and semioval center on 2D PC-MRI data has been performed with several other methods using similar algorithmic steps that have been incorporated in SELMA. These methods are tailored towards the analysis of one single artery at a time, require multiple voxels across the diameter of an artery, or require the need for manual delineation of the perforating arteries 36 – 38 . One method exists that uses convolutional neural networks to automatically segment perforating arteries 39 . SELMA has been developed specifically for the automatic analysis of small arteries with subvoxel size and pooling those to obtain metrics from them. Blood flow velocity in the smaller distal branches of the lenticulostrate arteries included in SELMA is more attenuated, which could explain the lower observed velocities (4.0-4.7 cm/s versus 7.0 cm/s 38 and 11.8 cm/s 36 ) and PI (0.27–0.49 versus 0.79 38 ) compared to those obtained with methods that (manually) delineated a larger singular branch of the lenticulostriate arteries. Arterial pulsatility is an important functional measure in neurodegenerative diseases such as cSVDs and can be quantified in a 2D plane, such as done in this study, or with a 4D flow method. The latter approach has been used in the larger intracranial arteries in cSVDs where associations between pulsatility and SVD markers were found 4 , 40 . In the perforating cerebral arteries, 4D flow is more difficult to implement due to the small vessel diameters, thus blood flow velocity in these smaller arteries are computed in a 2D plane and pulsatility is derived from the blood flow velocity alone. Especially in cSVDs, we found that the pulsatility of these small vessels are important markers for disease burden 18 , 19 . SELMA and its source code were made publically available and can be installed from: https://github.com/TNI-UMCU/SELMA/ and is provided with step-by-step installation instructions, including a user manual. The tool is supported on Windows and Linux. This study does have some limitations that have to be addressed. First, the results of our multi-vendor comparison and inter-rater reliability analyses are potentially affected by the differences in hardware, acquisition parameters and reconstruction settings across all sites and vendors. It is unclear how much this could have affected the manual ROI delineation in the basal ganglia or the manual artery censoring. However, these differences are assumed to affect the inter-site and inter-vendor differences in the outcome measures more than the inter-rater reliability. Future work on harmonization of the 2D PC-MRI sequence across multiple vendors, by e.g. using a vendor neutral MR pulse sequence program 41 , 42 , should address these differences to further stimulate the usage of these measurements of hemodynamic function in the perforating arteries in multicenter studies. Second, the SELMA approach to include subvoxel arteries in size might lead to underestimating the blood flow velocity due to partial volume effects 2 . It is assumed that the effect of underestimation is comparable between subjects and patients, thus still allowing the study of disease or physiology. Third, the relatively low number of included participants per site could also have affected the inter-site and inter-vendor comparisons for all outcome measures. We assume, however, that the contribution of the study population and sample size was small compared to the contribution of the sensitivity differences with regard to the variability in the outcome measures we found. Conclusion We present SELMA, a novel small cerebral artery blood flow velocity analysis tool of 2D PC-MRI data. We achieved good inter-rater reliability of the analysis on data acquired from MRI systems with different hardware, vendor, and scan parameters. Consistent and user-friendly analysis of small cerebral arteries is possible using SELMA. Differences in the implementation of 2D PC-MRI across vendors currently hampers multicenter studies on the hemodynamic function of the perforating arteries in the brain. Still, the tool can be used for single-center studies regardless of vendor or site in which 2D PC-MRI is performed in both patients and controls with the same system and protocol. Declarations Author Contribution S.D.T.P.: Conceptualization; Tool development; Formal analysis; Writing original draft; Final approval.C.C.: Tool development; Writing review & editing; Final approval.J.T.v.V.: Formal analysis; Writing review & editing; Final approval.R.J.v.T.: Formal analysis; Writing review & editing; Final approval.M.B., M.C., L.d.R., O.K., M.E.L., K.P., I.R., J.C.W.S., M.T., A.V.,: Data acquisition, Writing review & editing; Final approval.G.J.B.: Writing review & editing; Final approval.J.J.Z.: Conceptualization; Data acquisition; Formal analysis; Writing review & editing; Final approval. Acknowledgement This work received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC grant agreements nr. 337333 (SmallVesselMRI) and nr. 841865 (SELMA)S.D.T.P. and J.C.W.S. are funded by the UMCU Brain Center Young Talent Fellowship 2019. Data Availability The data that support the findings of this study are available, upon reasonable request, from the corresponding author. References Zwanenburg JJM, van Osch MJP. Targeting cerebral small vessel disease with mri. Stroke . 2017;48:3175-3182 Bouvy WH, Geurts LJ, Kuijf HJ, Luijten PR, Kappelle LJ, Biessels GJ, et al. Assessment of blood flow velocity and pulsatility in cerebral perforating arteries with 7-t quantitative flow mri. NMR Biomed. 2016;29:1295-1304 Mitchell GF, Van Buchem MA, Sigurdsson S, Gotal JD, Jonsdottir MK, Kjartansson Ó, et al. Arterial stiffness, pressure and flow pulsatility and brain structure and function: The age, gene/environment susceptibility – reykjavik study. Brain . 2011;134:3398-3407 O'Rourke MF, Hashimoto J. Mechanical factors in arterial aging: A clinical perspective. J Am Coll Cardiol . 2007;50:1-13 Webb AJS, Simoni M, Mazzucco S, Kuker W, Schulz U, Rothwell PM. Increased cerebral arterial pulsatility in patients with leukoaraiosis. Stroke . 2012;43:2631-2636 Van Sloten TT, Protogerou AD, Henry RMA, Schram MT, Launer LJ, Stehouwer CDA. Association between arterial stiffness, cerebral small vessel disease and cognitive impairment: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews . 2015;53:121-130 Zhai F-F, Ye Y-C, Chen S-Y, Ding F-M, Han F, Yang X-L, et al. Arterial stiffness and cerebral small vessel disease. Frontiers in Neurology . 2018;9 Chuang S-Y, Cheng H-M, Bai C-H, Yeh W-T, Chen J-R, Pan W-H. Blood pressure, carotid flow pulsatility, and the risk of stroke. Stroke . 2016;47:2262-2268 Aribisala BS, Morris Z, Eadie E, Thomas A, Gow A, Valdés Hernández MC, et al. Blood pressure, internal carotid artery flow parameters, and age-related white matter hyperintensities. Hypertension . 2014;63:1011-1018 Poels MMF, Zaccai K, Verwoert GC, Vernooij MW, Hofman A, Van Der Lugt A, et al. Arterial stiffness and cerebral small vessel disease. Stroke . 2012;43:2637-2642 Singer J, Trollor JN, Baune BT, Sachdev PS, Smith E. Arterial stiffness, the brain and cognition: A systematic review. Ageing Res Rev . 2014;15:16-27 Zeki Al Hazzouri A, Newman AB, Simonsick E, Sink KM, Sutton Tyrrell K, Watson N, et al. Pulse wave velocity and cognitive decline in elders. Stroke . 2013;44:388-393 Wardlaw JM, Smith C, Dichgans M. Small vessel disease: Mechanisms and clinical implications. Lancet Neurol . 2019;18:684-696 van den Brink H, Kopczak A, Arts T, Onkenhout L, Siero JCW, Zwanenburg JJM, et al. Cadasil affects multiple aspects of cerebral small vessel function on 7t-mri. Ann Neurol . 2023;93:29-39 Van Den Brink H, Pham S, Siero JC, Arts T, Onkenhout L, Kuijf H, et al. Assessment of small vessel function using 7t mri in patients with sporadic cerebral small vessel disease. Neurology . 2024;102 Perosa V, Arts T, Assmann A, Mattern H, Speck O, Oltmer J, et al. Pulsatility index in the basal ganglia arteries increases with age in elderly with and without cerebral small vessel disease. American Journal of Neuroradiology . 2022;43:540-546 Vikner T, Nyberg L, Holmgren M, Malm J, Eklund A, Wahlin A. Characterizing pulsatility in distal cerebral arteries using 4d flow mri. J Cereb Blood Flow Metab . 2020;40:2429-2440 Schnerr RS, Jansen JFA, Uludag K, Hofman PAM, Wildberger JE, Van Oostenbrugge RJ, et al. Pulsatility of lenticulostriate arteries assessed by 7 tesla flow mri—measurement, reproducibility, and applicability to aging effect. Frontiers in Physiology . 2017;8 van Hespen KM, Kuijf HJ, Hendrikse J, Luijten PR, Zwanenburg JJM. Blood flow velocity pulsatility and arterial diameter pulsatility measurements of the intracranial arteries using 4d pc-mri. Neuroinformatics . 2022;20:317-326 Köhler B, Born S, Van Pelt RFP, Hennemuth A, Preim U, Preim B. A survey of cardiac 4d pc‐mri data processing. Computer Graphics Forum . 2017;36:5-35 Köhler B, Grothoff M, Gutberlet M, Preim B. Bloodline: A system for the guided analysis of cardiac 4d pc-mri data. Computers & Graphics . 2019;82:32-43 Arts T, Siero JCW, Biessels GJ, Zwanenburg JJM. Automated assessment of cerebral arterial perforator function on 7t mri. J Magn Reson Imaging . 2021;53:234-241 Van Den Kerkhof M, Van Der Thiel MM, Van Oostenbrugge RJ, Postma AA, Kroon AA, Backes WH, et al. Impaired damping of cerebral blood flow velocity pulsatility is associated with the number of perivascular spaces as measured with 7t mri. Journal of Cerebral Blood Flow & Metabolism . 2023;43:937-946 Geurts L, Biessels GJ, Luijten P, Zwanenburg J. Better and faster velocity pulsatility assessment in cerebral white matter perforating arteries with 7t quantitative flow mri through improved slice profile, acquisition scheme, and postprocessing. Magn Reson Med . 2018;79:1473-1482 Arts T, Meijs TA, Grotenhuis H, Voskuil M, Siero J, Biessels GJ, et al. Velocity and pulsatility measures in the perforating arteries of the basal ganglia at 3t mri in reference to 7t mri. Front Neurosci . 2021;15:665480 Düzel E, Acosta‐Cabronero J, Berron D, Biessels GJ, Björkman‐Burtscher I, Bottlaender M, et al. European ultrahigh‐field imaging network for neurodegenerative diseases (eufind). Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring . 2019;11:538-549 Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment . 1994;6:284-290 Van Tuijl RJ, Pham SDT, Ruigrok YM, Biessels GJ, Velthuis BK, Zwanenburg JJM. Reliability of velocity pulsatility in small vessels on 3tesla mri in the basal ganglia: A test–retest study. Magnetic Resonance Materials in Physics, Biology and Medicine . 2022;36:15-23 Ma SJ, Sarabi MS, Yan L, Shao X, Chen Y, Yang Q, et al. Characterization of lenticulostriate arteries with high resolution black-blood t1-weighted turbo spin echo with variable flip angles at 3 and 7 tesla. NeuroImage . 2019;199:184-193 van Tuijl RJ, Ruigrok YM, Velthuis BK, van der Schaaf IC, Rinkel GJE, Zwanenburg JJM. Velocity pulsatility and arterial distensibility along the internal carotid artery. J Am Heart Assoc . 2020;9:e016883 Chengyue S, Yue W, Chen L, Zhiying X, Yunchuang S, Zhihao X, et al. Reduced blood flow velocity in lenticulostriate arteries of patients with cadasil assessed by pc-mra at 7t. Journal of Neurology, Neurosurgery &amp; Psychiatry . 2022;93:451 Zong X, Lin W. Quantitative phase contrast mri of penetrating arteries in centrum semiovale at 7t. NeuroImage . 2019;195:463-474 van den Kerkhof M, Jansen JFA, van Oostenbrugge RJ, Backes WH. 1d versus 3d blood flow velocity and pulsatility measurements of lenticulostriate arteries at 7t mri. Magnetic Resonance Imaging . 2023;96:144-150 Moore J, Jimenez J, Lin W, Powers W, Zong X. Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast mri at 7 t. Magnetic Resonance in Medicine . 2022;88:2088-2100 Tables Table 1: Standard clustering profiles for basal ganglia and semioval center 2D PC-MRI scans in SELMA SNR vel < - T n - T n < SNR vel T n Basal ganglia SNR mag > T n - - + SNR mag T n + - - SNR mag < T n + - - SNR mag = magnitude signal-to-noise ratio; SNR vel = velocity signal-to-noise ratio; T n = noise threshold Table 2: Desired scan parameters for the two dimensional phase contrast sequence for all three MRI vendors FOV RL x AP (mm) 250x250 Foldover direction AP Acquired matrix 832x833 Reconstructed voxel size (mm) 0.2 x 0.2 Parallel imaging none Slice thickness (mm) 2.0 TFE factor (nr. phase enc. lines per cardiac time point) 2 TR/TE (ms) 27/16 Flip angle 60 ° Cardiac synch. Retrospective triggering Reconstructed nr. of heart phases 14 Acquired temporal resolution 107 ms Venc (cm/s) 20 Scan duration 4 minutes with heart rate of 60 bpm Table 3: Results of the groupwise comparison between SELMA and the originally published results. Original SELMA N detected 24 ± 6 28 ± 6 v mean 3.9 ± 0.6 4.0 ± 0.5 PI 0.28 ± 0.08 0.27 ± 0.07 Values are given as mean ± SD. N detected = amount of detected arteries; v mean = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index Table 4: Scan parameters (mean ± SD) for the two-dimensional phase-contrast sequence for all three MRI vendors. Scan parameter Vendor 1 Vendor 2 Vendor 3 Head coil 32-channel receiver Circular polarized transmit coil 32-channel receiver Circular polarized transmit coil 24-channel / 32-channel receiver FOV, mm 242 ± 4 x 242 ± 4 250 x 250 154 ± 0.4 x 154 ± 0.4 Reconstructed voxel size, mm 0.23 x 0.23 0.2 x 0.2 0.3 x 0.3 Flip angle, ° 60 60 54-60 TR/TE, ms 32.4 ± 1.8 / 11.3 ± 0.5 26.3 ± 0.4 / 15.9 ± 0.4 28.1 ± 1.1 / 17.2 ± 0.4 Bandwidth, Hz/pixel 34.3 ± 1.6 59 60.8 ± 4.2 Echo Train Length 2 2 - 3 2 Acquired temporal resolution, ms 129.6 ± 7.2 128.7 ± 27.3 112.1 ± 4.3 Time points 12 ± 1 14 ± 1 14 Scan time, seconds 384 ± 72 242 ± 73 246 ± 39 Average heartrate/min 68 ± 16 60 ± 11 66 ± 10 Table 5: Outcome measures and inter-rater results per vendor Vendor 1 (n=10) Vendor 2 (n=20) Vendor 3 (n=30) All (n=60) Outcome measures N detected (n) 4 ± 3 18 ± 5 7 ± 5 10 ± 7 v mean (cm/s) 3.6 ± 1.3 3.8 ± 0.7 5.7 ± 1.4 4.7 ± 1.5 PI 0.87 ± 0.27 0.36 ± 0.11 0.46 ± 0.24 0.49 ± 0.27 SNR v 5.9 ± 2.5 6.7 ± 2.2 5.3 ± 1.5 5.9 ± 2.0 Inter-rater analysis DSC 0.88 [0.81 - 0.93] 0.93 [0.90 - 0.95] 0.90 [0.69 - 0.95] 0.91 [0.69 - 0.95] ICC N detected 0.75 [0.22 - 0.94] 0.72 [0.43 - 0.88] 0.88 [0.76 - 0.94] 0.92 [0.87 - 0.95] ICC v mean 0.80 [0.21 - 0.96] 0.75 [0.47 - 0.89] 0.76 [0.54 - 0.88] 0.84 [0.74 - 0.90] ICC PI 0.32 [-0.50 - 0.84] 0.79 [0.54 - 0.91] 0.93 [0.86 - 0.97] 0.85 [0.76 - 0.91] Values are given in mean ± SD for the outcome measures and mean with 95% confidence interval for the inter-rater analysis. N detected = amount of detected arteries; v mean = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index; ICC = intra-class coefficient; DSC = Dice similarity coefficient Table 6: Outcome measures per site Measurement Site 1 (n=10) Site 2 (n=2) Site 3 (n=9) Site 4 (n=9) Site 5 (n=5) Site 6 (n=6) Site 7 (n=10) Site 8 (n=9) N detected (n) 4 ± 3 17 ± 4 16 ± 5 21 ± 3 3 ± 2 4 ± 4 11 ± 3 7 ± 4 v mean ( cm/s) 3.6 ± 1.3 3.0 ± 0.3 4.0 ± 0.7 3.8 ± 0.5 6.6 ± 1.6 5.4 ± 1.2 4.9 ± 0.8 6.1 ± 1.6 PI 0.87 ± 0.27 0.43 ± 0.27 0.36 ± 0.11 0.34 ± 0.07 0.59 ± 0.56 0.46 ± 0.05 0.42 ± 0.14 0.45 ± 0.10 SNR v 5.9 ± 2.5 4.5 ± 2.0 7.6 ± 2.7 6.3 ± 1.2 5.1 ± 1.4 4.1 ± 0.8 6.2 ± 1.7 5.1 ± 1.2 Values are given as mean ± SD. N detected = amount of detected arteries; v mean = mean blood flow velocity of the perforating arteries given in cm/s and PI = pulsatility index Table 7: Coefficient of variation for inter-site and inter-vendor measurements. Measurement Inter-site Inter-vendor N detected (n) 62 31 v mean (cm/s) 21 12 PI 39 12 N detected = amount of detected arteries; v mean = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index; CV = coefficient of variation. Coefficients of variation are given as percentages. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Neuroinformatics → Version 1 posted Editorial decision: Revision requested 15 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviewers invited by journal 11 Sep, 2024 Editor assigned by journal 09 Sep, 2024 Submission checks completed at journal 09 Sep, 2024 First submitted to journal 06 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5045336","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362037317,"identity":"41732346-ff72-4fdf-8932-a4260cf1b3b3","order_by":0,"name":"S. D.T. Pham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACAxCRAMSMDz7AxBgbgAQ7Hi0HIFqYDWeA+GxgLUCDmAlrYZPmIUaLfAPzBuYPDHfy+KcdYJC2bdsmb3C/9+GDnzv+MJjj0GJwgK0AaMuzYonbCQzGuW23DTccYzc27D1jwGDZjMtVPCCHHU5suJ3/IRmoJcHgGBubNGObAYPBYVwOg2qZD7TlsCVEC/tvfFoYDkC1bLidwNjMCLWFGZ8Wg8NsBQfOGDxL3Hg7gZmx59xtw5nH0pgle9uMeXA6rL1544OKijuJ824nsP/4UXZbnu/wMcYPP9vk5AyON2DXAwzJA8Bww5TgweETuI8IyI+CUTAKRsGIBgBC8150CuN5IwAAAABJRU5ErkJggg==","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"D.T.","lastName":"Pham","suffix":""},{"id":362037318,"identity":"f48b1379-66c0-4813-b39a-d73f04120bf3","order_by":1,"name":"C. Chatziantoniou","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"","lastName":"Chatziantoniou","suffix":""},{"id":362037319,"identity":"ab2fc8ba-1469-4500-8cce-c1217b60b51c","order_by":2,"name":"J. T. Vliet","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"T.","lastName":"Vliet","suffix":""},{"id":362037320,"identity":"32f4c495-8119-4ab3-9136-472763a544d4","order_by":3,"name":"R. J. Tuijl","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"J.","lastName":"Tuijl","suffix":""},{"id":362037321,"identity":"505ffc45-4d9a-4e92-ba8b-cbd04eb3e760","order_by":4,"name":"M. Bulk","email":"","orcid":"","institution":"University Medical Center Leiden","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Bulk","suffix":""},{"id":362037322,"identity":"190a8b00-5b50-47e6-8fec-d27e627a6361","order_by":5,"name":"I. Ronen","email":"","orcid":"","institution":"Brighton and Sussex Medical School","correspondingAuthor":false,"prefix":"","firstName":"I.","middleName":"","lastName":"Ronen","suffix":""},{"id":362037323,"identity":"2e57900f-9370-4e12-9d03-6af7b1540f43","order_by":6,"name":"M. E. Ladd","email":"","orcid":"","institution":"German Cancer Research Center (DKFZ)","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"E.","lastName":"Ladd","suffix":""},{"id":362037324,"identity":"616a7288-f5e3-4388-b90e-fd0ba393b3ac","order_by":7,"name":"O. Kraff","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"O.","middleName":"","lastName":"Kraff","suffix":""},{"id":362037325,"identity":"f2af4da5-56fe-434b-bfcc-b40119018b33","order_by":8,"name":"L. de Rochefort","email":"","orcid":"","institution":"Aix Marseille University, CNRS, CRMBM","correspondingAuthor":false,"prefix":"","firstName":"L.","middleName":"","lastName":"de Rochefort","suffix":""},{"id":362037326,"identity":"fddd5872-b31d-44aa-b5e0-283ad6871224","order_by":9,"name":"A. Villringer","email":"","orcid":"","institution":"Max Planck Institute for Human Cognitive and Brain Sciences","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Villringer","suffix":""},{"id":362037327,"identity":"927fe748-3fa5-4d55-b0ac-0f1937922f20","order_by":10,"name":"K. Pine","email":"","orcid":"","institution":"Max Planck Institute for Human Cognitive and Brain Sciences","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"","lastName":"Pine","suffix":""},{"id":362037329,"identity":"ac8b6ec6-e6cb-4540-a311-3af7257aa09c","order_by":11,"name":"J.C.W. Siero","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"J.C.W.","middleName":"","lastName":"Siero","suffix":""},{"id":362037334,"identity":"a52e8019-bb01-4cef-bd0e-6c400b75b06d","order_by":12,"name":"M. Costagli","email":"","orcid":"","institution":"IRCCS Ospedale Policlinico San Martino","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Costagli","suffix":""},{"id":362037337,"identity":"eff51829-600f-491c-904f-c4874886371a","order_by":13,"name":"M. Tosetti","email":"","orcid":"","institution":"IRCCS Stella Maris","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Tosetti","suffix":""},{"id":362037339,"identity":"ba7f36e0-178a-401a-b6cc-a28d86680851","order_by":14,"name":"G.J. Biessels","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"G.J.","middleName":"","lastName":"Biessels","suffix":""},{"id":362037341,"identity":"39fc813f-d9c8-424d-ac02-7e1ee47fe540","order_by":15,"name":"J.J.M. Zwanenburg","email":"","orcid":"","institution":"University Medical Center Utrecht, Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"J.J.M.","middleName":"","lastName":"Zwanenburg","suffix":""}],"badges":[],"createdAt":"2024-09-06 16:03:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5045336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5045336/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12021-024-09703-4","type":"published","date":"2025-01-22T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67199342,"identity":"832b7f94-078e-4cb8-8dc6-c6b4f9de96f1","added_by":"auto","created_at":"2024-10-22 09:39:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":634194,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5045336/v1/d7bb1b7c86028baf31372bc1.png"},{"id":67199343,"identity":"38f3bb7b-972c-4d56-9e3e-b0fba2a2fa75","added_by":"auto","created_at":"2024-10-22 09:39:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1936266,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5045336/v1/886ae06097258f9d26ca2019.png"},{"id":67199340,"identity":"008431ce-3aaf-479d-9a43-b2138c2f8ff6","added_by":"auto","created_at":"2024-10-22 09:39:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290357,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5045336/v1/41003f655cc996054290a631.png"},{"id":67199339,"identity":"120a006b-7225-4b7d-bf82-777678e5fa0e","added_by":"auto","created_at":"2024-10-22 09:39:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":256034,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5045336/v1/400fa613e9400d1ef0aa7716.png"},{"id":74859011,"identity":"f5fc17f6-33a8-4ffa-89aa-dee29d7f0a38","added_by":"auto","created_at":"2025-01-27 16:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4943675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5045336/v1/a6357c2e-c362-44e8-b5e5-d04dcc6d4db0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood flow velocity analysis in cerebral perforating arteries on 7T 2D phase contrast MRI with an open-source software tool (SELMA)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral blood flow in large intracranial arteries can be quantified using phase contrast magnetic resonance imaging (PC-MRI). This technique estimates the blood flow velocity from the MR signal's phase accrual of moving spins as the velocity of the moving spins (i.e. blood) is proportional to this phase accrual. Recently, with the advantages of the increased sensitivity on high-field 7T MRI, granting higher spatial resolutions with adequate SNR, blood flow velocity of the brain's perforating arteries through a two-dimensional plane can be measured using 2D PC-MRI\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood flow velocity measurements can inform on the condition of these arteries, notably also in disease states\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In particular, the blood flow velocity's pulsatility index (PI) is an important indicator of arterial stiffness\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Increased stiffness in the large intracranial arteries has been associated with damage to the brain parenchyma, such as microbleeds\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, lacunar infarcts\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and white matter hyperintensities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, while also being linked to cognitive impairment\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and cognitive decline\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the smaller arteries, information from these blood flow velocity measurements could be especially relevant in cerebral small vessel diseases (cSVDs), an important cause of stroke and dementia\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recently, using 7T MRI, an increased velocity PI in the perforating arteries was found in patients with cSVDs and a lower blood flow velocity was linked to white matter damage increase/progression\u003csup\u003e18, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Age was also found to be a determinant of increased velocity PI in the perforating arteries of the basal ganglia in both cSVD and general population cohorts\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor the larger intracranial arteries, several publicly available image processing tools exist to analyze PC-MRI data\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For the smaller arteries, however, there are no processing tools available yet. Reported analyses of PC-MRI data of the smaller arteries were performed with a collection of in-house developed code\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which has several limitations. First, such code consists of scripts in which the parameters of the algorithms are hard-coded and not accessible as user-defined settings. Second, these scripts, including parameter settings, often evolve with time without explicit version tracking. Also, as part of the developments of the analysis methods, several variants of output parameters were computed in parallel, such as the PI from the mean normalized velocity traces from all arteries and the mean over the PIs of the individual velocity traces. This situation can potentially create ambiguities and unintentionally result in inconsistent application of output parameters in studies. Additionally, it impedes the reproducibility of previous analyses, even more so across centers. Finally, the lack of version-controlled software with a user-friendly user interface hampers the dissemination of the methods to other researchers, which limits the usage of blood flow velocity and PI measurements in the perforating arteries of the basal ganglia and semioval center on a larger scale. To support consistent, repeatable analysis and to promote the usage and possibly further development of the PC-MRI measurements in small arteries, an analysis tool should be established that is accessible (open-source), user-friendly (with a graphical user interface (GUI)), and facilitate repeatable analysis (logging of version and settings with results) of small artery PC-MRI data.\u003c/p\u003e \u003cp\u003eThis work presents the Small vessEL MArker (SELMA) analysis software as a novel, open-source tool for cerebral small artery flow velocity analysis compatible with data from multiple MRI vendors. SELMA incorporates converged and updated analysis algorithms used in our previous publications\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. To validate the implementation of the algorithm in SELMA, we re-analyzed previously published data\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and compared the results of SELMA with the original results obtained with a previously published iteration of the algorithm. Additionally, we assessed the inter-rater reliability of SELMA on data from three different 7T MRI vendors by two trained operators. A secondary aim was to assess the performance of measuring perforating artery velocity pulsatility at MRI scanners from different sites and vendors by comparing measurements between sites and testing for age and sex effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eAlgorithm\u003c/h2\u003e\n \u003cp\u003eThe original algorithm used to analyze small artery PC-MRI data as described in previous work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e was developed in MATLAB (version R2015b, the MathWorks, Natick, MA). These MATLAB scripts have been rewritten in Python 3.7 to develop SELMA for analysis of the cerebral perforating arteries at the level of the basal ganglia or the white matter at the semioval center (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). SELMA was developed to be compatible with all 2D PC-MRI DICOM data, regardless of MRI vendor or field strength. The implementation of the algorithm in SELMA follows that of the original codes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, but includes several updates and improvements, which will be detailed below.\u003c/p\u003e\n \u003cp\u003eAnalysis in SELMA can be started by drawing a region of interest (ROI) on the 2D PC-MRI images using the software interface or loading such mask from the disk. The first step of the analysis is the estimation of the noise level in the phase and magnitude frames, which is computed on a voxel-wise basis using the standard deviation of the complex signal (constructed from the phase and magnitude data) over the cardiac cycle. The root mean square of the standard deviations of the imaginary and complex parts of the constructed complex signal are computed next. A median filter with a user-defined kernel size (default 10 mm) is then applied to the root mean square maps. The temporal signal-to-noise ratio of the magnitude frames (SNR\u003csub\u003emag\u003c/sub\u003e) is computed by dividing the magnitude frames with the median filtered root mean square maps. The phase maps are scaled to velocity maps with the pre-defined velocity encoding (v\u003csub\u003eenc\u003c/sub\u003e) of the scan protocol parameters available from the DICOM header. The velocity frames are averaged over the cardiac cycle and median-filtered with a 10 mm kernel. The median filtered velocity map is subtracted from the raw velocity maps at each point in the cardiac cycle to remove the background phase of static tissue and to center the velocity around zero. Next, using SNR\u003csub\u003emag\u003c/sub\u003e, the standard deviation of the corrected velocity maps (\u0026sigma;\u003csub\u003ev\u003c/sub\u003e) can be computed using the following formula\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{\\sigma\\:}_{v}=\\frac{{v}_{enc}}{\\pi\\:}\\frac{1}{{SNR}_{mag}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe corrected velocity maps are divided by \u0026sigma;\u003csub\u003ev\u003c/sub\u003e to obtain the temporal signal-to-noise ratio of the velocity frames (SNR\u003csub\u003ev\u003c/sub\u003e). Based on the SNR\u003csub\u003emag\u003c/sub\u003e and SNR\u003csub\u003ev\u003c/sub\u003e, voxels in the 2D PC-MRI image can be clustered into several profiles (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For the detection of vessels in the basal ganglia, clusters of voxels with an SNR\u003csub\u003emag\u003c/sub\u003e and SNR\u003csub\u003ev\u003c/sub\u003e above a positive noise threshold (T\u003csub\u003en\u003c/sub\u003e) are detected as potential arteries. Clusters of voxels in the semioval center are identified as arteries only if SNR\u003csub\u003ev\u003c/sub\u003e is lower than the negative T\u003csub\u003en\u003c/sub\u003e, regardless of SNR\u003csub\u003emag\u003c/sub\u003e. The sign for SNR\u003csub\u003ev\u003c/sub\u003e in the semioval center is changed due to the opposite directionality of the blood flow of the perforating arteries at the semioval center compared to the basal ganglia. T\u003csub\u003en\u003c/sub\u003e can be changed by the user by changing the significance level in the settings (default 0.05, in which case Tn\u0026thinsp;=\u0026thinsp;1.96). A new addition in SELMA is the scaling of T\u003csub\u003en\u003c/sub\u003e based on the number of acquired heart phases and the temporal resolution of the data. Estimation of the SNR in the magnitude and velocity frames are dependent on the heart rate, which affects the number of acquired heart phases, and on the number of k-space lines acquired per heartbeat and TR of the acquisition, which affects the temporal resolution.\u003c/p\u003e\n \u003cp\u003eAfter identification of the arteries that exceed T\u003csub\u003en\u003c/sub\u003e, operators of SELMA can select the options to automatically filter out arteries whose orientation is far from being perpendicular to the scanning plane and/or to deduplicate arteries that are too close to each other. The blood flow velocity in arteries that are not perpendicular to the scanning plane will be underestimated\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. SELMA considers the artery\u0026apos;s roundness to determine whether the artery can be considered as perpendicular to the scanning plane, at least in first approximation, in which case it should be included in the analysis. An ellipse is fitted to the artery\u0026apos;s circumference, and the axes ratio of the ellipse is computed, which discards the artery from further analysis if the axes ratio exceeds a user-set threshold (default 2). Artery detections that are too close to each other might be multiple detections on a single artery oriented parallel to the scanning plane or might be caused by ghosting artefacts. In such cases, SELMA discards all arteries except the one with the highest velocity if detected within a user-set distance (default 1.2 mm) from each other. These options can be applied both to basal ganglia and semioval center focussed PC-MRI scans. Additional options can be selected to erode the outer region of the ROI and/or filter out ghosting artefacts from the scans. Arteries in the outer edges of the ROI in the semioval center are more prone to motion artifacts. Users can specify how many voxels from the edges of the ROI can be eroded (default 80). Ghosting of larger arteries in the phase encoding direction can lead to erroneous artery detections. SELMA incorporates the automatic ghosting censoring method as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. First, SELMA identifies the large blood arteries by applying a relative intensity threshold on the magnitude images. Only clusters of voxels in the top magnitude percentile defined by the user (default 0.3%) and larger than a minimum size are included as potential large arteries. Depending on the size of the bright voxel cluster, an exclusion zone will be drawn around the cluster. Undesired detections in these zones will be discarded from further analysis. Ghosting artefacts are a larger issue in the semioval center when automatically segmented ROIs are used compared to the basal ganglia where they can be avoided when manually delineating a ROI. In the options, users can define custom values for the thresholds, voxel sizes of the arteries or length and width of the exclusion zones.\u003c/p\u003e\n \u003cp\u003eAnother addition to SELMA is the ability for operators to censor artery detections in the basal ganglia manually. Selecting this option will override the in-plane artery censoring and deduplication steps. SELMA will visualize all detected arteries and guide the user systematically through every artery prompting the user if it should be discarded for analysis. The user can see the proximity of the artery to others in the user interface, and the axes ratio of the artery is provided to aid the user in this process. The user can opt to keep or discard the highlighted artery, and is then guided to the next one. After all detected arteries have been evaluated, SELMA prompts the user to either save their results, or to restart the manual vessel censoring process again from the beginning. All parts of the manual vessel censoring functionality can be directly performed in the GUI.\u003c/p\u003e\n \u003cp\u003eAfter final artery selection, the number of perforating arteries (N\u003csub\u003edetected\u003c/sub\u003e), their mean blood flow velocity (v\u003csub\u003emean\u003c/sub\u003e) and the velocity PI are assessed. The PI is generally defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{v}_{max}-{v}_{min}}{{v}_{mean}}\\)\u003c/span\u003e\u003c/span\u003e, where v\u003csub\u003emax\u003c/sub\u003e, v\u003csub\u003emin\u003c/sub\u003e, and v\u003csub\u003emean\u003c/sub\u003e, are the maximum, minimum, and mean of the velocity trace over the cardiac cycle in cm/s. We calculated the PI from the averaged velocity trace of all final included arteries, while the individual velocity traces were normalized before averaging (hence, v\u003csub\u003emean\u003c/sub\u003e in the PI formula was 1.0 by definition). During the development of SELMA, an approach using the median of the normalized velocity trace was also considered, however during internal testing, we found that the blood flow velocity distribution over all arteries for each time point in the cardiac cycle followed a normal distribution and that using a median estimator had a higher uncertainty than the mean estimator, which led to inflated values of the PI\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Thus, we opted to use the mean of the normalized velocity trace to compute the PI. During internal testing, we observed similar group differences in blood flow velocity and velocity PI using the mean versus median normalized velocity trace.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eUser interface\u003c/h3\u003e\n\u003cp\u003eAnalysis of small vessel 2D PC-MRI data has been made more user friendly with the addition of a GUI in SELMA (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). 2D PC-MRI scans of either the basal ganglia or the semioval center in DICOM format can be loaded into SELMA via the menu in the top bar. ROIs that were either manually drawn in SELMA or segmented with external software, such as white matter masks, can also be loaded from the disk with this menu. Analysis can be started with the \u0026apos;Analyse\u0026apos; option in the menu for single scans or, if masks are already provided, entire folders containing scans. The batch analysis option in SELMA allows for automatic analysis of multiple scans without any user input, drastically speeding up the analysis process. Two default voxel clustering settings have been defined, tailored to acquisitions at the basal ganglia and semioval center, respectively. Before the analysis, the actual slice location must be selected in the bottom right-hand corner to ensure that the corresponding predefined voxel clustering settings are applied in SELMA. Custom clustering options can also be selected in this menu for analysis of data that do not fall into the standard profiles for basal ganglia or semioval center scans. The remaining options in the top menu allow for changing the contrast or zoom of the image, which can also be directly manipulated in the user interface using the mouse. In the settings menu, the operator can change several options and thresholds as described in the \u0026apos;Algorithm\u0026apos; section.\u003c/p\u003e\n\u003cp\u003eSELMA was developed specifically with the goal of an easy to maintain code base. To this end, the GUI and the processing parts of the code are intentionally separated in different classes that can only talk to each other using the Signal-and-Slot paradigm. This should enable future developers to easily understand the code, and make small changes to either the processing algorithms or the GUI without accidentally introducing unwanted behavior elsewhere in the program.\u003c/p\u003e\n\u003ch3\u003eTest data\u003c/h3\u003e\n\u003cp\u003eThe implementation of the analysis algorithm in SELMA was validated with 7T MRI (Achieva 7T, Philips Medical Systems, Best, The Netherlands) data from previous work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These 2D PC-MRI data were acquired with a 32-channel receive head coil (Nova Medical, Wilmington, NC, United States) in 14 patients with coarctation of the aorta and 15 control subjects with no history of cardiovascular disease, neurological disease, or intellectual disability. The 2D PC sequence was planned on a 3D T1-weighted image at the level of the basal ganglia (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and was retrospectively gated using a peripheral pulse oximeter for triggering. The following scan parameters were used: 250 \u0026times; 250 mm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e field of view; acquired spatial resolution 0.3 \u0026times; 0.3 \u0026times; 2.0 mm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, reconstructed spatial resolution (through zero-filling in k-space) 0.2 \u0026times; 0.2 \u0026times; 2.0 mm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e; TR/TE\u0026thinsp;=\u0026thinsp;28/14.7\u0026ndash;15.1 ms; flip angle\u0026thinsp;=\u0026thinsp;50\u0026deg;; TFE factor\u0026thinsp;=\u0026thinsp;2 (i.e. number of k-lines acquired per cardiac frame, per heart beat); velocity encoding\u0026thinsp;=\u0026thinsp;20 cm/s; acquired temporal resolution\u0026thinsp;=\u0026thinsp;112 ms; 13\u0026ndash;15 reconstructed heart phases, depending on the heart rate; SENSE factor\u0026thinsp;=\u0026thinsp;1.5; scan duration was about 5 minutes for a heart rate of 60 beats/min.\u003c/p\u003e\n\u003cp\u003eInter-rater reliability was assessed with data from 8 different scanning sites comprising three different MRI vendors: Philips (Achieva 7T, Philips Healthcare, Best, The Netherlands), Siemens (MAGNETOM 7T, Siemens Healthineers, Erlangen, Germany) and General Electric (GE) (Discovery MR950, GE Healthcare, Chicago, Illinois, USA). All MRI data were acquired with a 32-channel receive head coil (Nova Medical, Wilmington, NC, United States), except for one site where a 24-channel receive head coil (Nova Medical, Wilmington, NC, United States) was used. All scanning sites participated in the European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. EUFIND comprises researchers from 22 7T MRI sites in Europe with the common aim of identifying opportunities and challenges of 7T MRI for clinical and research applications in neurodegeneration. Each site included healthy elderly subjects aged between 50 and 70 years with an equal gender distribution and attempted to include an equal number of volunteers between 50 and 60 years and between 60 and 70 years to allow for secondary analysis of age and gender effects on the outcome parameters. All institutions performed the scanning under the approval of the institution\u0026apos;s local ethical review board, and all subjects provided written informed consent.\u003c/p\u003e\n\u003cp\u003eThe 2D PC-MRI sequence was originally developed for the Philips 7T MRI and the protocol was harmonized across all sites with the parameters in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Harmonization was limited by the vendor-specific implementation of the 2D PC sequence and parameter ranges. The acquisition slice was planned in the basal ganglia, targeting the perforating lenticulostriate arteries that branch from the circle of Willis. Representative basal ganglia scans per vendor are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eThe differences in the outcome measures between SELMA and the previously published results on the validation data were quantified on a group level with a Bland-Altman analysis. Inter-rater reliability of the ROIs drawn in SELMA was assessed using the Dice similarity coefficient (DSC) between ROIs drawn by two trained operators. Inter-rater reliability of the outcome measures using manual artery selection was assessed by calculating the Intra-class Correlation Coefficient (ICC) between two operators. An ICC above 0.75 was considered to be an excellent correlation between operators\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The coefficient of variation (CV) of all outcome measures and the SNR\u003csub\u003ev\u003c/sub\u003e of the included arteries averaged over all subjects within each site were used to assess inter-site differences. The CV for all sites within a single vendor was computed and subsequently averaged over all vendors to assess the inter-vendor differences. The relation of age and sex with the outcome measures was assessed using a linear mixed model corrected with site and vendor as random effects. All statistical analyses were performed in MATLAB (version R2021a, the MathWorks, Natick, MA) except for the linear mixed modelling, which was performed in R (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria) with the \u0026apos;lme4\u0026apos; package.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eValidation of the implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows the results of the original measurements compared to SELMA on previously published data of 29 participants\u003csup\u003e24\u003c/sup\u003e. In the same data, SELMA had a slightly higher N\u003csub\u003edetected\u003c/sub\u003e compared to the previous version of the algorithm. The v\u003csub\u003emean\u003c/sub\u003e and velocity PI were similar between both approaches at a group level. Bland-Altman plots for the comparison between the original measurements and SELMA are shown in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-vendor comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData of 60 participants (mean age \u0026plusmn; standard deviation: 59\u0026plusmn;6 years) were included (eight sites, three MRI vendors) for the multi-vendor analysis. Due to vendor-specific implementation of the 2D PC sequence, scan parameters slightly varied between subject, site and vendor. Table 4 provides an overview of the relevant parameters. The N\u003csub\u003edetected\u003c/sub\u003e, v\u003csub\u003emean,\u003c/sub\u003e PI, and SNR\u003csub\u003ev\u003c/sub\u003e are stratified per vendor in Table 5 and per site in Table 6. The CV for inter-site differences were larger for all measurements than the inter-vendor differences (Table 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-rater analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean DSC for the manually drawn ROIs by the two operators was 0.91 (range 0.69-0.95) across all 60 participants (Table 4). All ICCs for the measurements exceeded 0.75 except for the velocity PI of vendor 1 and N\u003csub\u003edetected\u0026nbsp;\u003c/sub\u003eof vendor 2. The ICC of N\u003csub\u003edetected\u003c/sub\u003e, v\u003csub\u003emean\u003c/sub\u003e, and velocity PI for the entire dataset was 0.92, 0.84, and 0.85, respectively (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge and gender analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge and gender were not significantly associated with N\u003csub\u003edetected\u003c/sub\u003e, v\u003csub\u003emean\u003c/sub\u003e, and velocity PI over the entire population in multivariate models adjusted for site and vendor (Figure 4).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed SELMA, an open-source small cerebral artery analysis tool for 2D PC-MRI data. We have shown that the results using SELMA match well with the results of the original version of our algorithm using published data\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Reproducible multi-vendor analysis of 2D PC-MRI data was also possible with SELMA.\u003c/p\u003e \u003cp\u003eWith SELMA, we obtained a higher N\u003csub\u003edetected\u003c/sub\u003e than in the previous analysis, which can be explained by the changes that were made in the deduplication steps. Previously, all perforating arteries within a 1.2 mm distance from each other were discarded in the analysis, which led to more false negatives during detection. In SELMA, only the artery with the highest v\u003csub\u003emean\u003c/sub\u003e is kept, and all other detections within a 1.2 mm distance are discarded. The increase in N\u003csub\u003edetected\u003c/sub\u003e had no effect on the average v\u003csub\u003emean\u003c/sub\u003e and velocity PI, as these were similar to the original results over the entire group. This supports the original assumption that averaging over all available small arteries gives a representative number for the detected population of arteries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The Limits of Agreement (LoA) for the velocity PI found in the Bland-Altman analysis was similar to the LoA on a group-level for the original measurements on 7T (LoA original versus SELMA: 0.20 vs 0.16)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, suggesting that the variability in velocity PI can mostly be explained by thermal or physiological noise during the measurement rather than a bias introduced by different analysis methods.\u003c/p\u003e \u003cp\u003eOn multi-vendor data, analysis with SELMA showed that the CV in the outcome measures was higher between sites than between vendors. The CV for N\u003csub\u003edetected\u003c/sub\u003e was higher than the other outcome measures in both comparisons. The high variance in N\u003csub\u003edetected\u003c/sub\u003e can be explained by various factors causing the sensitivity differences for detecting arteries across sites. Differences in the reconstruction software of the scanners could likely partially explain this variation. The variation of N\u003csub\u003edetected\u003c/sub\u003e between sites of the same vendor might reflect variation in patient handling by the local MRI technician, such as the usage of padding to limit motion, which was not standardized and may have varied between sites. Also, despite slice planning was standardized via an illustrated instruction, variations between technicians in the execution of this planning cannot be prevented and could effect the number of visible vessels. Another source of variation could be that the protocol could slightly vary between sites as gradient performance or the availability of (commercial) options in the software could vary and the protocol at each site was implemented as close to the requested parameters as possible. All these factors could have contributed to the variance of N\u003csub\u003edetected\u003c/sub\u003e and these differences in sensitivity illustrate the challenges in harmonizing the 2D PC-MRI sequence across multiple sites and vendors, as shown in the differences in scan parameters between sites and vendors (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and SNR\u003csub\u003ev\u003c/sub\u003e differences (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn an early attempt by the EUFIND group, the 2D PC-MRI sequence was also attempted to harmonize for the perforating arteries in the semioval center. However, large sensitivity differences between vendors made the implementation of this sequence in these small (\u0026le;\u0026thinsp;300\u0026micro;m) perforating arteries difficult\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These sensitivity differences might also affect the CVs of the outcome measures of the perforating arteries in the basal ganglia. The v\u003csub\u003emean\u003c/sub\u003e and velocity PI over the entire dataset were slightly higher than reported for control groups in other studies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This can likely be attributed to the high variance in N\u003csub\u003edetected\u003c/sub\u003e due to the aforementioned sensitivity differences across sites. Smaller arteries with a blood flow velocity just above the noise threshold might not be detected in several sites, which could lead to an overestimation of v\u003csub\u003emean\u003c/sub\u003e and velocity PI in these participants. A similar effect has been observed when measuring these perforating arteries at 3T MRI\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe mean DSC for the manually drawn ROIs on all scans across all sites and vendors (mean: 0.91; range: 0.69\u0026ndash;0.95) was comparable to earlier results on 3T 2D PC-MRI data which was single-vendor only (mean: 0.90; range: 0.85\u0026ndash;0.95)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The option to manually censor the results of the automatic artery detection in the basal ganglia was added after the observation that the automatic detection could not always successfully remove in-plane arteries (i.e. arteries not perpendicular to the imaging plane). The lenticulostriate arteries in the basal ganglia are quite tortuous\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which can be a challenge for the automatic in-plane artery removal and deduplication steps in SELMA. The arteries in the semioval center are smaller than in the basal ganglia and are censored better with the automatic censoring steps. The operator can freely choose which arteries to include or exclude with manual censoring. The ICCs after manual artery censoring exceeded 0.75 for all outcome measurements over the entire dataset, indicating that despite the differences in acquisition parameters in the entire dataset, manual censoring yields good to excellent inter-rater reliability. The overall ICC for the velocity PI was higher than the reported ICCs for test-retest reliability by Schnerr et al\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e., suggesting that the SELMA results are probably less dependent on the operator (manual ROI delineation and vessel censoring) than on the measurement noise (scan-to-scan variability).\u003c/p\u003e \u003cp\u003eWe found no relation between age or gender and the outcome measures when corrected for sites and vendors using a linear mixed model. In literature, velocity PI of the lenticulostriate arteries has been found to be positively associated with age\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and males had a higher velocity PI in the internal carotid arteries\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e but not in the lenticulostriate arteries\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e compared to females. The lack of an age effect on the velocity PI in our dataset could be explained by both the differences in acquisition parameters and sensitivity between sites and vendors and the relatively small age range of the study participants in our study (mean age: 59; range: 50\u0026ndash;70; standard deviation: 6 years) when compared to the other studies in the literature\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In an additional analysis, we found velocity PI to be associated with N\u003csub\u003edetected\u003c/sub\u003e in our dataset (Supplementary Table\u0026nbsp;1). As a result of the differences in acquisition between sites, velocity PI seems to be dependent on N\u003csub\u003edetected\u003c/sub\u003e, which could also explain the lack of an age effect on the velocity PI. The association between N\u003csub\u003edetected\u003c/sub\u003e and PI was negative, which suggests that a lower N\u003csub\u003edetected\u003c/sub\u003e means that only the relatively larger vessels (with accordingly higher PI) are detected.\u003c/p\u003e \u003cp\u003eAnalysis of cerebral perforating arteries of the basal ganglia and semioval center on 2D PC-MRI data has been performed with several other methods using similar algorithmic steps that have been incorporated in SELMA. These methods are tailored towards the analysis of one single artery at a time, require multiple voxels across the diameter of an artery, or require the need for manual delineation of the perforating arteries\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. One method exists that uses convolutional neural networks to automatically segment perforating arteries\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. SELMA has been developed specifically for the automatic analysis of small arteries with subvoxel size and pooling those to obtain metrics from them. Blood flow velocity in the smaller distal branches of the lenticulostrate arteries included in SELMA is more attenuated, which could explain the lower observed velocities (4.0-4.7 cm/s versus 7.0 cm/s\u003csup\u003e38\u003c/sup\u003e and 11.8 cm/s\u003csup\u003e36\u003c/sup\u003e) and PI (0.27\u0026ndash;0.49 versus 0.79\u003csup\u003e38\u003c/sup\u003e) compared to those obtained with methods that (manually) delineated a larger singular branch of the lenticulostriate arteries.\u003c/p\u003e \u003cp\u003eArterial pulsatility is an important functional measure in neurodegenerative diseases such as cSVDs and can be quantified in a 2D plane, such as done in this study, or with a 4D flow method. The latter approach has been used in the larger intracranial arteries in cSVDs where associations between pulsatility and SVD markers were found\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In the perforating cerebral arteries, 4D flow is more difficult to implement due to the small vessel diameters, thus blood flow velocity in these smaller arteries are computed in a 2D plane and pulsatility is derived from the blood flow velocity alone. Especially in cSVDs, we found that the pulsatility of these small vessels are important markers for disease burden\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSELMA and its source code were made publically available and can be installed from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/TNI-UMCU/SELMA/\u003c/span\u003e\u003cspan address=\"https://github.com/TNI-UMCU/SELMA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and is provided with step-by-step installation instructions, including a user manual. The tool is supported on Windows and Linux.\u003c/p\u003e \u003cp\u003eThis study does have some limitations that have to be addressed. First, the results of our multi-vendor comparison and inter-rater reliability analyses are potentially affected by the differences in hardware, acquisition parameters and reconstruction settings across all sites and vendors. It is unclear how much this could have affected the manual ROI delineation in the basal ganglia or the manual artery censoring. However, these differences are assumed to affect the inter-site and inter-vendor differences in the outcome measures more than the inter-rater reliability. Future work on harmonization of the 2D PC-MRI sequence across multiple vendors, by e.g. using a vendor neutral MR pulse sequence program\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, should address these differences to further stimulate the usage of these measurements of hemodynamic function in the perforating arteries in multicenter studies. Second, the SELMA approach to include subvoxel arteries in size might lead to underestimating the blood flow velocity due to partial volume effects\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is assumed that the effect of underestimation is comparable between subjects and patients, thus still allowing the study of disease or physiology. Third, the relatively low number of included participants per site could also have affected the inter-site and inter-vendor comparisons for all outcome measures. We assume, however, that the contribution of the study population and sample size was small compared to the contribution of the sensitivity differences with regard to the variability in the outcome measures we found.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe present SELMA, a novel small cerebral artery blood flow velocity analysis tool of 2D PC-MRI data. We achieved good inter-rater reliability of the analysis on data acquired from MRI systems with different hardware, vendor, and scan parameters. Consistent and user-friendly analysis of small cerebral arteries is possible using SELMA. Differences in the implementation of 2D PC-MRI across vendors currently hampers multicenter studies on the hemodynamic function of the perforating arteries in the brain. Still, the tool can be used for single-center studies regardless of vendor or site in which 2D PC-MRI is performed in both patients and controls with the same system and protocol.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.D.T.P.: Conceptualization; Tool development; Formal analysis; Writing original draft; Final approval.C.C.: Tool development; Writing review \u0026amp; editing; Final approval.J.T.v.V.: Formal analysis; Writing review \u0026amp; editing; Final approval.R.J.v.T.: Formal analysis; Writing review \u0026amp; editing; Final approval.M.B., M.C., L.d.R., O.K., M.E.L., K.P., I.R., J.C.W.S., M.T., A.V.,: Data acquisition, Writing review \u0026amp; editing; Final approval.G.J.B.: Writing review \u0026amp; editing; Final approval.J.J.Z.: Conceptualization; Data acquisition; Formal analysis; Writing review \u0026amp; editing; Final approval.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work received funding from the European Research Council under the European Union\u0026rsquo;s Seventh Framework Program (FP7/2007-2013)/ERC grant agreements nr. 337333 (SmallVesselMRI) and nr. 841865 (SELMA)S.D.T.P. and J.C.W.S. are funded by the UMCU Brain Center Young Talent Fellowship 2019.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available, upon reasonable request, from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZwanenburg JJM, van Osch MJP. Targeting cerebral small vessel disease with mri. \u003cem\u003eStroke\u003c/em\u003e. 2017;48:3175-3182\u003c/li\u003e\n\u003cli\u003eBouvy WH, Geurts LJ, Kuijf HJ, Luijten PR, Kappelle LJ, Biessels GJ, et al. Assessment of blood flow velocity and pulsatility in cerebral perforating arteries with 7-t quantitative flow mri. \u003cem\u003eNMR Biomed.\u003c/em\u003e 2016;29:1295-1304\u003c/li\u003e\n\u003cli\u003eMitchell GF, Van Buchem MA, Sigurdsson S, Gotal JD, Jonsdottir MK, Kjartansson \u0026Oacute;, et al. Arterial stiffness, pressure and flow pulsatility and brain structure and function: The age, gene/environment susceptibility \u0026ndash; reykjavik study. \u003cem\u003eBrain\u003c/em\u003e. 2011;134:3398-3407\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Rourke MF, Hashimoto J. Mechanical factors in arterial aging: A clinical perspective. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2007;50:1-13\u003c/li\u003e\n\u003cli\u003eWebb AJS, Simoni M, Mazzucco S, Kuker W, Schulz U, Rothwell PM. Increased cerebral arterial pulsatility in patients with leukoaraiosis. \u003cem\u003eStroke\u003c/em\u003e. 2012;43:2631-2636\u003c/li\u003e\n\u003cli\u003eVan Sloten TT, Protogerou AD, Henry RMA, Schram MT, Launer LJ, Stehouwer CDA. Association between arterial stiffness, cerebral small vessel disease and cognitive impairment: A systematic review and meta-analysis. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e. 2015;53:121-130\u003c/li\u003e\n\u003cli\u003eZhai F-F, Ye Y-C, Chen S-Y, Ding F-M, Han F, Yang X-L, et al. Arterial stiffness and cerebral small vessel disease. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e. 2018;9\u003c/li\u003e\n\u003cli\u003eChuang S-Y, Cheng H-M, Bai C-H, Yeh W-T, Chen J-R, Pan W-H. Blood pressure, carotid flow pulsatility, and the risk of stroke. \u003cem\u003eStroke\u003c/em\u003e. 2016;47:2262-2268\u003c/li\u003e\n\u003cli\u003eAribisala BS, Morris Z, Eadie E, Thomas A, Gow A, Vald\u0026eacute;s Hern\u0026aacute;ndez MC, et al. Blood pressure, internal carotid artery flow parameters, and age-related white matter hyperintensities. \u003cem\u003eHypertension\u003c/em\u003e. 2014;63:1011-1018\u003c/li\u003e\n\u003cli\u003ePoels MMF, Zaccai K, Verwoert GC, Vernooij MW, Hofman A, Van Der Lugt A, et al. Arterial stiffness and cerebral small vessel disease. \u003cem\u003eStroke\u003c/em\u003e. 2012;43:2637-2642\u003c/li\u003e\n\u003cli\u003eSinger J, Trollor JN, Baune BT, Sachdev PS, Smith E. Arterial stiffness, the brain and cognition: A systematic review. \u003cem\u003eAgeing Res Rev\u003c/em\u003e. 2014;15:16-27\u003c/li\u003e\n\u003cli\u003eZeki Al Hazzouri A, Newman AB, Simonsick E, Sink KM, Sutton Tyrrell K, Watson N, et al. Pulse wave velocity and cognitive decline in elders. \u003cem\u003eStroke\u003c/em\u003e. 2013;44:388-393\u003c/li\u003e\n\u003cli\u003eWardlaw JM, Smith C, Dichgans M. Small vessel disease: Mechanisms and clinical implications. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2019;18:684-696\u003c/li\u003e\n\u003cli\u003evan den Brink H, Kopczak A, Arts T, Onkenhout L, Siero JCW, Zwanenburg JJM, et al. Cadasil affects multiple aspects of cerebral small vessel function on 7t-mri. \u003cem\u003eAnn Neurol\u003c/em\u003e. 2023;93:29-39\u003c/li\u003e\n\u003cli\u003eVan Den Brink H, Pham S, Siero JC, Arts T, Onkenhout L, Kuijf H, et al. Assessment of small vessel function using 7t mri in patients with sporadic cerebral small vessel disease. \u003cem\u003eNeurology\u003c/em\u003e. 2024;102\u003c/li\u003e\n\u003cli\u003ePerosa V, Arts T, Assmann A, Mattern H, Speck O, Oltmer J, et al. Pulsatility index in the basal ganglia arteries increases with age in elderly with and without cerebral small vessel disease. \u003cem\u003eAmerican Journal of Neuroradiology\u003c/em\u003e. 2022;43:540-546\u003c/li\u003e\n\u003cli\u003eVikner T, Nyberg L, Holmgren M, Malm J, Eklund A, Wahlin A. Characterizing pulsatility in distal cerebral arteries using 4d flow mri. \u003cem\u003eJ Cereb Blood Flow Metab\u003c/em\u003e. 2020;40:2429-2440\u003c/li\u003e\n\u003cli\u003eSchnerr RS, Jansen JFA, Uludag K, Hofman PAM, Wildberger JE, Van Oostenbrugge RJ, et al. Pulsatility of lenticulostriate arteries assessed by 7 tesla flow mri\u0026mdash;measurement, reproducibility, and applicability to aging effect. \u003cem\u003eFrontiers in Physiology\u003c/em\u003e. 2017;8\u003c/li\u003e\n\u003cli\u003evan Hespen KM, Kuijf HJ, Hendrikse J, Luijten PR, Zwanenburg JJM. Blood flow velocity pulsatility and arterial diameter pulsatility measurements of the intracranial arteries using 4d pc-mri. \u003cem\u003eNeuroinformatics\u003c/em\u003e. 2022;20:317-326\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hler B, Born S, Van Pelt RFP, Hennemuth A, Preim U, Preim B. A survey of cardiac 4d pc‐mri data processing. \u003cem\u003eComputer Graphics Forum\u003c/em\u003e. 2017;36:5-35\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hler B, Grothoff M, Gutberlet M, Preim B. Bloodline: A system for the guided analysis of cardiac 4d pc-mri data. \u003cem\u003eComputers \u0026amp; Graphics\u003c/em\u003e. 2019;82:32-43\u003c/li\u003e\n\u003cli\u003eArts T, Siero JCW, Biessels GJ, Zwanenburg JJM. Automated assessment of cerebral arterial perforator function on 7t mri. \u003cem\u003eJ Magn Reson Imaging\u003c/em\u003e. 2021;53:234-241\u003c/li\u003e\n\u003cli\u003eVan Den Kerkhof M, Van Der Thiel MM, Van Oostenbrugge RJ, Postma AA, Kroon AA, Backes WH, et al. Impaired damping of cerebral blood flow velocity pulsatility is associated with the number of perivascular spaces as measured with 7t mri. \u003cem\u003eJournal of Cerebral Blood Flow \u0026amp; Metabolism\u003c/em\u003e. 2023;43:937-946\u003c/li\u003e\n\u003cli\u003eGeurts L, Biessels GJ, Luijten P, Zwanenburg J. Better and faster velocity pulsatility assessment in cerebral white matter perforating arteries with 7t quantitative flow mri through improved slice profile, acquisition scheme, and postprocessing. \u003cem\u003eMagn Reson Med\u003c/em\u003e. 2018;79:1473-1482\u003c/li\u003e\n\u003cli\u003eArts T, Meijs TA, Grotenhuis H, Voskuil M, Siero J, Biessels GJ, et al. Velocity and pulsatility measures in the perforating arteries of the basal ganglia at 3t mri in reference to 7t mri. \u003cem\u003eFront Neurosci\u003c/em\u003e. 2021;15:665480\u003c/li\u003e\n\u003cli\u003eD\u0026uuml;zel E, Acosta‐Cabronero J, Berron D, Biessels GJ, Bj\u0026ouml;rkman‐Burtscher I, Bottlaender M, et al. European ultrahigh‐field imaging network for neurodegenerative diseases (eufind). \u003cem\u003eAlzheimer\u0026apos;s \u0026amp; Dementia: Diagnosis, Assessment \u0026amp; Disease Monitoring\u003c/em\u003e. 2019;11:538-549\u003c/li\u003e\n\u003cli\u003eCicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. \u003cem\u003ePsychological Assessment\u003c/em\u003e. 1994;6:284-290\u003c/li\u003e\n\u003cli\u003eVan Tuijl RJ, Pham SDT, Ruigrok YM, Biessels GJ, Velthuis BK, Zwanenburg JJM. Reliability of velocity pulsatility in small vessels on 3tesla mri in the basal ganglia: A test\u0026ndash;retest study. \u003cem\u003eMagnetic Resonance Materials in Physics, Biology and Medicine\u003c/em\u003e. 2022;36:15-23\u003c/li\u003e\n\u003cli\u003eMa SJ, Sarabi MS, Yan L, Shao X, Chen Y, Yang Q, et al. Characterization of lenticulostriate arteries with high resolution black-blood t1-weighted turbo spin echo with variable flip angles at 3 and 7 tesla. \u003cem\u003eNeuroImage\u003c/em\u003e. 2019;199:184-193\u003c/li\u003e\n\u003cli\u003evan Tuijl RJ, Ruigrok YM, Velthuis BK, van der Schaaf IC, Rinkel GJE, Zwanenburg JJM. Velocity pulsatility and arterial distensibility along the internal carotid artery. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2020;9:e016883\u003c/li\u003e\n\u003cli\u003eChengyue S, Yue W, Chen L, Zhiying X, Yunchuang S, Zhihao X, et al. Reduced blood flow velocity in lenticulostriate arteries of patients with cadasil assessed by pc-mra at 7t. \u003cem\u003eJournal of Neurology, Neurosurgery \u0026amp;amp;amp; Psychiatry\u003c/em\u003e. 2022;93:451\u003c/li\u003e\n\u003cli\u003eZong X, Lin W. Quantitative phase contrast mri of penetrating arteries in centrum semiovale at 7t. \u003cem\u003eNeuroImage\u003c/em\u003e. 2019;195:463-474\u003c/li\u003e\n\u003cli\u003evan den Kerkhof M, Jansen JFA, van Oostenbrugge RJ, Backes WH. 1d versus 3d blood flow velocity and pulsatility measurements of lenticulostriate arteries at 7t mri. \u003cem\u003eMagnetic Resonance Imaging\u003c/em\u003e. 2023;96:144-150\u003c/li\u003e\n\u003cli\u003eMoore J, Jimenez J, Lin W, Powers W, Zong X. Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast \u0026lt;scp\u0026gt;mri\u0026lt;/scp\u0026gt; at 7 t. \u003cem\u003eMagnetic Resonance in Medicine\u003c/em\u003e. 2022;88:2088-2100\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Standard clustering profiles for basal ganglia and semioval center 2D PC-MRI scans in SELMA\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"503\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003evel\u003c/sub\u003e \u0026lt; -\u003c/strong\u003e T\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e T\u003csub\u003en\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp;\u0026lt; SNR\u003csub\u003evel\u003c/sub\u003e \u0026lt;\u0026nbsp;\u003c/strong\u003eT\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003evel\u003c/sub\u003e \u0026gt;\u0026nbsp;\u003c/strong\u003eT\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 503px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal ganglia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003emag\u003c/sub\u003e \u0026gt; T\u003csub\u003en\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003emag\u003c/sub\u003e \u0026lt; T\u003csub\u003en\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 503px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentrum semioval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003emag\u003c/sub\u003e \u0026gt; T\u003csub\u003en\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003csub\u003emag\u003c/sub\u003e \u0026lt; T\u003csub\u003en\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSNR\u003csub\u003emag\u0026nbsp;\u003c/sub\u003e= magnitude signal-to-noise ratio; SNR\u003csub\u003evel\u003c/sub\u003e = velocity signal-to-noise ratio; T\u003csub\u003en\u0026nbsp;\u003c/sub\u003e= noise threshold\u003c/p\u003e\n\u003cp\u003eTable 2: Desired scan parameters for the two dimensional phase contrast sequence for all three MRI vendors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"425\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOV RL x AP (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e250x250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFoldover direction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquired matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e832x833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReconstructed voxel size (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e0.2 x 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParallel imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlice thickness (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFE factor (nr. phase enc. lines per cardiac time point)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTR/TE (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e27/16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlip angle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e60\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac synch.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003eRetrospective triggering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReconstructed nr. of heart phases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquired temporal resolution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e107 ms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVenc (cm/s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScan duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5294%;\"\u003e\n \u003cp\u003e4 minutes with heart rate of 60 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3: Results of the groupwise comparison between SELMA and the originally published results.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"284\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.1549%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.4507%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOriginal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3944%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSELMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.1549%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003csub\u003edetected\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.4507%;\"\u003e\n \u003cp\u003e24 \u0026plusmn; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3944%;\"\u003e\n \u003cp\u003e28 \u0026plusmn; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.1549%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ev\u003csub\u003emean\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.4507%;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3944%;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.1549%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.4507%;\"\u003e\n \u003cp\u003e0.28 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3944%;\"\u003e\n \u003cp\u003e0.27 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are given as mean\u0026nbsp;\u0026plusmn; SD.\u0026nbsp;N\u003csub\u003edetected\u003c/sub\u003e = amount of detected arteries; v\u003csub\u003emean\u003c/sub\u003e = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index\u003c/p\u003e\n\u003cp\u003eTable 4: Scan parameters (mean \u0026plusmn; SD) for the two-dimensional phase-contrast sequence for all three MRI vendors.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScan parameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHead coil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e32-channel receiver\u003c/p\u003e\n \u003cp\u003eCircular polarized transmit coil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e32-channel receiver\u003c/p\u003e\n \u003cp\u003eCircular polarized transmit coil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e24-channel / 32-channel receiver\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOV, mm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e242 \u0026plusmn; 4 x 242 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e250 x 250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e154 \u0026plusmn; 0.4 x 154 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReconstructed voxel size, mm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.23 x 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.2 x 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.3 x 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlip angle, \u0026deg;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e54-60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTR/TE, ms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e32.4 \u0026plusmn; 1.8 / 11.3 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e26.3 \u0026plusmn; 0.4 / 15.9 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.1 \u0026plusmn; 1.1 / 17.2 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBandwidth, Hz/pixel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e34.3 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e60.8 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcho Train Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2 - 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquired temporal resolution, ms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e129.6 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e128.7 \u0026plusmn; 27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e112.1 \u0026plusmn; 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime points\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e12 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e14 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScan time, seconds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e384 \u0026plusmn; 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e242 \u0026plusmn; 73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e246 \u0026plusmn; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage heartrate/min\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e68 \u0026plusmn; 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e60 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e66 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Outcome measures and inter-rater results per vendor\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 1 (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 2 (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor 3 (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN\u003csub\u003edetected\u003c/sub\u003e (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 \u0026plusmn; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e18 \u0026plusmn; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7 \u0026plusmn; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e10 \u0026plusmn; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ev\u003csub\u003emean\u003c/sub\u003e (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.7 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.46 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.49 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSNR\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-rater analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.88 [0.81 - 0.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.93 [0.90 - 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.90 [0.69 - 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.91 [0.69 - 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eICC N\u003csub\u003edetected\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.75 [0.22 - 0.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.72 [0.43 - 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.88 [0.76 - 0.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.92 [0.87 - 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eICC v\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.80 [0.21 - 0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.75 [0.47 - 0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.76 [0.54 - 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.84 [0.74 - 0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eICC PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.32 [-0.50 - 0.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.79 [0.54 - 0.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.93 [0.86 - 0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.85 [0.76 - 0.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are given in\u0026nbsp;mean \u0026plusmn; SD for the outcome measures and\u0026nbsp;mean with 95% confidence interval for the inter-rater analysis.\u0026nbsp;N\u003csub\u003edetected\u003c/sub\u003e = amount of detected arteries; v\u003csub\u003emean\u003c/sub\u003e = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index; ICC = intra-class coefficient; DSC = Dice similarity coefficient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6: Outcome measures per site\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2439%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1 (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 2 (n=2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 3 (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 4 (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 5 (n=5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 6 (n=6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 7 (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2134%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite 8 (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2439%;\"\u003e\n \u003cp\u003eN\u003csub\u003edetected\u003c/sub\u003e (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e4 \u0026plusmn; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e17 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6707%;\"\u003e\n \u003cp\u003e16 \u0026plusmn; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e21 \u0026plusmn; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e3 \u0026plusmn; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e4 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e11 \u0026plusmn; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2134%;\"\u003e\n \u003cp\u003e7 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2439%;\"\u003e\n \u003cp\u003ev\u003csub\u003emean (\u003c/sub\u003ecm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6707%;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e5.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2134%;\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2439%;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e0.43 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6707%;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e0.34 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e0.59 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e0.46 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e0.42 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2134%;\"\u003e\n \u003cp\u003e0.45 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2439%;\"\u003e\n \u003cp\u003eSNR\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6707%;\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e6.3 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e5.1 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9756%;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5183%;\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2134%;\"\u003e\n \u003cp\u003e5.1 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are given as mean \u0026plusmn; SD.\u0026nbsp;N\u003csub\u003edetected\u003c/sub\u003e = amount of detected arteries; v\u003csub\u003emean\u003c/sub\u003e = mean blood flow velocity of the perforating arteries given in cm/s and PI = pulsatility index\u003c/p\u003e\n\u003cp\u003eTable 7:\u0026nbsp;Coefficient of variation for inter-site and inter-vendor measurements.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"290\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7698%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-vendor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7698%;\"\u003e\n \u003cp\u003eN\u003csub\u003edetected\u0026nbsp;\u003c/sub\u003e(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7698%;\"\u003e\n \u003cp\u003ev\u003csub\u003emean\u003c/sub\u003e (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7698%;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6151%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN\u003csub\u003edetected\u003c/sub\u003e = amount of detected arteries; v\u003csub\u003emean\u003c/sub\u003e = mean blood flow velocity of the perforating arteries given in cm/s; PI = pulsatility index; CV = coefficient of variation. Coefficients of variation are given as percentages.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neuroinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nein","sideBox":"Learn more about [Neuroinformatics](http://link.springer.com/journal/12021)","snPcode":"12021","submissionUrl":"https://submission.nature.com/new-submission/12021/3","title":"Neuroinformatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5045336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5045336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland-Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (v\u003csub\u003emean\u003c/sub\u003e) and velocity PI of SELMA was very similar to the original results (v\u003csub\u003emean\u003c/sub\u003e: mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 cm/s; velocity PI: mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) despite the slightly higher number of detected vessels in SELMA (N\u003csub\u003edetected\u003c/sub\u003e: mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 4\u0026thinsp;\u0026plusmn;\u0026thinsp;9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69\u0026ndash;0.95) and the overall intra-class coefficient for N\u003csub\u003edetected\u003c/sub\u003e, v\u003csub\u003emean\u003c/sub\u003e, and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels.\u003c/p\u003e","manuscriptTitle":"Blood flow velocity analysis in cerebral perforating arteries on 7T 2D phase contrast MRI with an open-source software tool (SELMA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 09:38:51","doi":"10.21203/rs.3.rs-5045336/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-15T13:18:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T17:45:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125792630552451914593711728160413672139","date":"2024-09-11T15:31:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-11T14:46:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-09T05:21:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-09T05:21:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neuroinformatics","date":"2024-09-06T16:01:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neuroinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nein","sideBox":"Learn more about [Neuroinformatics](http://link.springer.com/journal/12021)","snPcode":"12021","submissionUrl":"https://submission.nature.com/new-submission/12021/3","title":"Neuroinformatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ef8632d0-84a8-416e-9226-c7e7c8433564","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:08:29+00:00","versionOfRecord":{"articleIdentity":"rs-5045336","link":"https://doi.org/10.1007/s12021-024-09703-4","journal":{"identity":"neuroinformatics","isVorOnly":false,"title":"Neuroinformatics"},"publishedOn":"2025-01-22 15:57:23","publishedOnDateReadable":"January 22nd, 2025"},"versionCreatedAt":"2024-10-22 09:38:51","video":"","vorDoi":"10.1007/s12021-024-09703-4","vorDoiUrl":"https://doi.org/10.1007/s12021-024-09703-4","workflowStages":[]},"version":"v1","identity":"rs-5045336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5045336","identity":"rs-5045336","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}