Dynamic Contrast-Enhanced MRA of the aorta using a Golden-Angle Radial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved Cartesian MRA (TWIST)

Dynamic Contrast-Enhanced MRA of the aorta using a Golden-Angle Radial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved Cartesian MRA (TWIST) | 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 Dynamic Contrast-Enhanced MRA of the aorta using a Golden-Angle Radial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved Cartesian MRA (TWIST) Camilla Calastra, Elena Kleban, Fabrice Helfenstein, Fabian Haupt, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4306592/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Oct, 2024 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose: to compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic diseases patient cohort: the conventional Cartesian-sampling-based, TWIST sequence, and the radial-sampling-based GRASP sequence. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and consequently are expected to improve the image quality in body parts subjected to motion. Methods: 1.5T magnetic resonance angiography data from thirty patients (60.9±16.1y.o.) were assessed to investigate image quality as well as spatial and temporal blurring in the ascending aorta (AA), descending aorta (DA) and abdominal aorta (AbA). Results: GRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis (TWIST, reader 1: 1.6±0.5; reader 2: 1.9±0.4; reader 3: 1.1±0.4; GRASP, reader 1: 1.5±0.5; reader 2: 1.4±0.5; reader 3: 1.0±0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9±0.6; reader 2: 1.6±0.6; reader 3: 2.0±0.3; GRASP, reader 1: 1.4±0.6; reader 2: 1.2±0.4; reader 3: 1.3±0.6) and quantitative analysis (TWIST, AA=0.12±0.04, DA=0.12±0.03, AbA=0.11±0.03; GRASP, AA=0.20±0.05, DA=0.22±0.06, AbA = 0.20±0.05). Streaking artefacts of GRASP were stronger visible compared to TWIST (TWIST, reader 1: 2.2±0.6; reader 2: 1.9±0.3; reader 3: 2.0±0.5; GRASP, reader 1: 2.6±0.6; reader 2: 2.3±0.5; reader 3: 2.8±0.6). Conclusion: GRASP outperformed TWIST in SNR, vessel sharpness and reduction in image blurring; streaking artifacts were stronger visible with GRASP, but did not affect diagnostic image quality. contrast enhanced time resolved MRA thoracic imaging aortic diseases radial trajectory GRASP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gadolinium-based (Gd) contrast-enhanced time-resolved magnetic resonance angiography (CE-trMRA) techniques are able to depict the anatomy and haemodynamics of complex vascular structures [1-8]. While computed tomography angiography (CTA) with its inherent ionising radiation dose remains the reference method for thoracic imaging in an acute clinical setting [1, 9-12], CE-trMRA stands as a valuable alternative to CTA avoiding ionizing radiation and providing dynamic contrast information [1-3, 5, 8]. Within aortic disease populations, its application holds substantial promise for advancing clinical care by delivering real-time insights into blood flow patterns. Beyond its diagnostic capabilities, CE-trMRA emerges as a tool for monitoring disease progression over time [2-3]. This is invaluable for making informed decisions about the timing and necessity of interventions. Furthermore, CE-trMRA plays a pivotal role in treatment planning [1,5]. This facilitates more targeted and effective treatment strategies, enhancing overall patient care. In particular, the real-time assessment of aortic caliber, aortic aneurysms and various aortic side branches prove to be of paramount importance for diagnostic accuracy and to guide interventions with a focus on individual patient needs [4]. The TWIST (Time-resolved angiography With Interleaved Stochastic Trajectories) sequence is a commonly used acquisition technique to perform CE-trMRA measurements. It is based on a Cartesian acquisition with sharing of k-space data between adjacent time frames to allow for a good compromise between spatial and temporal resolution [13]. The drawback of a Cartesian trajectory is its susceptibility to image artefacts in presence of respiratory motion, such as spatial blurring of vascular boundaries [14]. Acquisition techniques based on radial sampling are less sensitive to motion than those based on Cartesian sampling and consequently improve the overall image quality in presence of motion [15]. Moreover, undersampling artifacts with radial imaging manifest as streaks, whereas they appear as aliasing with Cartesian. In contrast to Cartesian imaging, artefacts appear as incoherent aliasing in multiple dimensions [16], which is well suited for 4D image reconstruction based on compressed sensing (CS) exploiting spatial and temporal correlations and using a nonlinear reconstruction to enforce sparsity in a suitable transform domain [17, 18]. A uniform coverage of the k-space with high temporal incoherence can be obtained for any number of views, if the golden angle rotation is applied between successive echoes [19]. This enables dynamic imaging studies using continuous data acquisition and retrospective reconstruction of image series with arbitrary temporal resolution by grouping different numbers of consecutive echoes into temporal frames. The GRASP (Golden-angle RAdial Sparse Parallel) magnetic resonance imaging (MRI) technique combines the above mentioned aspects by using a stack-of-stars acquisition with a golden angle trajectory in combination with compressed sensing and parallel imaging reconstruction [20]. Previous works have successfully proven that GRASP allows for a reliable and robust assessment of dynamic CE liver imaging [21, 22] at high acceleration factors. Also, GRASP is known to provide robust imaging in situations with potential image degradation because of respiratory, cardiac or vascular motion artifacts [23]. Because of the above mentioned advantages, GRASP has a potential in improving image quality in body regions subjected to motion and for imaging of high-flow vascular pathologies like peripheral arteriovenous malformations (AVMs), that demand -high temporal resolution while maintaining a decent spatial resolution [24]. Hence, the purpose of our intra-individual study is to investigate the potential advantages of GRASP over TWIST in patients with aortic diseases. To this end, a qualitative comparison including inter- and intra-observer variabilities as well as quantitative comparison between images acquired by conventional TWIST and GRASP sequences has been performed. Materials and Methods In this cross-over single-centre retrospective study, a cohort of 30 patients (60.9±16.1 yo, 7 females) with various chronic aortic diseases (aortic dissection type A: 10, aortic dissection type B: 7, aortic aneurysm: 8; status after contained aortic rupture: 3, Marfan syndrome: 1, stenosis of left subclavian artery and impact of coronary artery bypass: 1) underwent a clinical follow up routine MRI examination between July and October 2022. The standard TWIST was complemented by a GRASP sequence after the study was approved by the local institutional review board and all patients gave their written informed consent prior to the MRI examination. MRI data acquisition All data was acquired on a 1.5T clinical scanner (Magnetom SolaFit, Siemens, Erlangen, Germany) equipped with a 32 channel body coil. With each CE-trMRA acquisition the same amount of Gd contrast agent (CA) (Gadovist 1.0M, Bayer, Switzerland AG, Zurich) was administered (0.075 ml/(kg bw), flow rate 4 ml/s), i.e. twice during each protocol. All images were acquired during free breathing in the oblique coronal plane. To reduce bias due contrast enhancement in the vascular systems during the second CA administration, TWIST and GRASP sequences were acquired in reverse order for half of the patients (n=15), respectively. For all examinations, there was a three minutes pause between CA administrations.Reconstruction of GRASP data has been performed inline at the scanner within about 30 s. All acquisition parameters are summarised in Table 1. For the TWIST sequence, 25% of the k-space center was used to reconstruct a time frame, whereas 33% of the remaining k-space periphery was sampled between each acquisition of k-space center [25]. For the GRASP sequence, 13 radial projections were used to reconstruct one time frame. Qualitative image analysis Qualitative analysis was performed using original non-subtracted 3D dynamic images. Three experienced radiologists (with 20 (#1), 5 (#2), and 8 (#3) years of experience) assessed the overall image quality independently. To this end, vascular contrast, vessel sharpness and image artefacts of TWIST and GRASP were assessed by grading the images on a 4-point Likert scale. In detail: for overall image quality, vascular contrast and vessel sharpness: 1=excellent; 2=acceptable (good); 3=poor (still diagnostic); 4=non-diagnostic . For image artefacts: 1=no artefacts; 2=minor artefacts (not interfering with diagnostic content), 3=moderate artefacts (degrading diagnostic content, image still diagnostic), 4=severe artefacts (non-diagnostic image). When assessing image artefacts special attention was paid to streaking artefacts for GRASP and fold-over artefacts for TWIST. For the overall image quality index, readers focused on vascular enhancement visibility and perfusion on the vessel of the ascending aorta, supra-aortic vessels, intercostal arteries, visceral branches, particularly inferior mesenteric artery, renal cortex (primary entry and additional small communication channels). Quantitative image analysis Quantitative image analysis was performed by MATLAB 9.12 (MathWorks, Natick, MA, USA) using original non-subtracted images. Circular regions-of-interest (ROIs) were placed at three aortic levels as shown in Figure 1: ascending aorta (AA), descending aorta at the level of the pulmonary trunk (DA), and abdominal aorta at the level of the infrarenal arteries (AbA). In cases of an aortic dissection the true lumen was chosen for the placement of the ROIs. When drawing circular ROIs, those slices and temporal frames were selected, where the anatomy of interest was best visible for the readers. For each patient identical ROIs at the same positions were used for both sequences. ROIs were drawn on TWIST images first for half of the patients. The temporal behaviour of the sequences was quantified by obtaining the maximum slope of the contrast agent uptake c (maxslope) and the full width at half maximum (FWHM) from the normalised and interpolated signal intensity time courses within the ROIs . Time signal intensity curves were interpolated by a factor of 10 and a smoothing function was applied to exclude the influence of noise on the outcome. Spatial blurring was quantified by calculating vessel sharpness as follows: at the same levels of the aorta as described above a straight line perpendicular to the vessel wall was drawn across the aorta. Vessel sharpness ( vs) of the boundary was then calculated from the resulting signal intensity profile as vs=1/d. The value d (millimetres) is the distance between those points on the drawn line, between which the signal intensity changed from 20% to 80% of the absolute intensity difference, i.e. the difference between the maximal and the minimal signal intensity values [26] (Figure 2). Signal-to-Noise ratio (SNR) calculation was performed by adopting the first method described in the previous work [27]. For noise, images from the second and the third time frames (prior to the bolus onset) were subtracted. The first time frame was not used as it has slightly different acquisition parameters for TWIST.. Statistical analysis Mean and standard deviation values were calculated for all outcomes of the qualitative and quantitative assessment. Statistical analysis was performed using R, version R 4.2.2 (R Foundation for Statistical Computing, 2021) and Excel, version 16.66.1 (22101101), (Microsoft, Redmond, Washington, USA). For all tests, the statistically significant difference is set to p < 0.05. Multilevel mixed-effect proportional-odds models were used for the qualitative analysis. Multilevel mixed-effect proportional-odds models include the scores as an ordinal dependent variable, the sequence (GRASP vs. TWIST) as the fixed factor and patient pseudo-ID and reader ID as random factors. The link function is logit (proportional odds). Results Results of the qualitative assessment are summarised in Table 2, 3 and 4. Table 2 reports the mean value and standard deviation for each qualitative outcome for each reader., Table 3 reports the results of the multilevel mixed-effect proportional odds cumulative logic models. GRASP sequence is better than TWIST for vascular contrast and vessel sharpness (negative model estimates; scores are lower with GRASP than with TWIST; all p < 0.001). TWIST is superior than TWIST (positive model estimates; scores are higher with GRASP than with TWIST) for image artifacts (p < 0.001). Even if severe image artefacts are characteristic for the data acquired using GRASP sequence, these do not influence the diagnostics as corroborated by the image quality index (p = 0.35). Table 4 reports the results of inter-reader analysis: readers’ agreement is random. Figure 1 shows the evolution of signal intensity values for the ascending aorta at the level of the pulmonary trunk (AA, red curves), the descending aorta at the levels of pulmonary trunk (DA, blue curves) and the abdominal aorta at the level of infrarenal arteries (AbA, green curves) for GRASP and TWIST. The signal intensity values obtained with the GRASP sequence are higher than those obtained with the TWIST sequence. In addition, the corresponding time-courses are smoother. Figure 3 shows the varying amount of streaking artefacts for GRASP sequence at different levels of aorta. Representative images acquired with GRASP and TWIST sequences are reported in Figure 4. As for artefacts observed in GRASP sequence, readers observed streaking artefacts in GRASP sequence and blurring of sharp contrast or object edges in TWIST sequence. Furthermore, readers observed a combination of breathing and GRAPPA artefacts. In Figure 5 quantitative results are summarised. GRASP sequence achieved superior values in vessel sharpness and SNR. FWHM was better on images produced with the TWIST sequence and there are three outliers for both sequences. Maximum slope of the CA uptake was similar for both sequences. Discussion The intra-individual study focused on the evaluation of a golden angle (GA) radial CE-trMRA sequence as an alternative for the conventional Cartesian sequence for imaging the aorta in patients with aortic diseases. Our results suggest that the GRASP sequence improved image quality of the aorta despite the presence of streaking artifacts (with its current temporal resolution), which is in agreement with a previous study [18]. Shown by the quantification of vessel sharpness and SNR, the depiction of the aorta is superior to conventional TWIST imaging, also corroborated by the scoring results of the experienced radiologists. A very moderate low-pass filtering in the temporal domain across the areas of interest has been observed for GRASP, whereas the uptake of CA described by the maximum slope is comparable in the two sequences. To our knowledge there is no published study describing GRASP applied to large vessels. Imaging artefacts The artefact level is significantly higher in the GRASP images (Table 2, 3 and Figure 3). This is caused by streaking artefacts as a result of high level of undersampling [18]. Using a higher number of radial projections may reduce the undersampling factor and mitigate the streaking artefacts at a cost of lower temporal resolution. Therefore, adapting the number of radial projections for the required temporal resolution for each applications is beneficial for image quality; e.g. a somewhat lower temporal resolution may be sufficient for aortic applications while resulting in a lower level of streaking artefacts. Streaking artefacts are more pronounced in the periphery of the FoV. Within the region of interest, the ascending aorta and the aortic arch are most affected. As for artefacts observed in TWIST sequence, blurring of sharp contrast or object edges is due to the lower resolution in phase encoding direction and its Cartesian nature. The breathing and GRAPPA artefacts on TWIST are likely due to coil sensitivity changes during breathing [28]. Spatial appearance Improved vessel sharpness, both qualitative and quantitative (Table 2 and 3 and Figure 5), for all ROIs locations in GRASP compared to TWIST is assumed to be a result of the higher in-plane resolution and the reduced sensitivity of the radial trajectory to breathing motion. In addition, image sharpness of the GRASP sequence is influenced by the choice of density compensation function for the balance of the k-space energy content and by the spatial weight in the CS reconstruction [29,30]. However, this cannot be controlled due to a standardised vendor reconstruction. Temporal behaviour of the contrast enhancement The analysis of the temporal behaviour of both sequences revealed a significantly lower FWHM of the first-pass bolus for TWIST in all locations compared to GRASP (Figure 5), despite the higher temporal resolution for GRASP and the shared view of TWIST that can generate temporal blurring [31]. This may be a result of the shorter acquisition of the k-space center (scan time for the 25% of k-space center used to reconstruct one time frame ~1s) compared to GRASP, where k-space center crossing spokes (during the temporal resolution of 1.8s) were used to reconstruct a time frame. Furthermore, the CS reconstruction of GRASP sequence includes a temporal regularization term, which may introduce temporal blurring. However, it turned out by the qualitative analysis of vascular contrast (Table 2 and Table 3) that the observed differences in the dynamics are not of clinical relevance. Interestingly, two outliers for the FWHM values at all aorta locations are observed. A more detailed assessment of these data revealed abnormal flow direction in these patient due to the compressed true lumen of the aorta. No significant differences between sequences was found for the maximum upslope (Figure 5) of the first-pass bolus. Additionally, the results of FWHM and max slope may not have direct clinical relevance but corroborate some clinical observations and might be relevant for quantification of some aspects [32] and hence could be useful for clinicians. e.g. quantification of aspects of vascular malformations would be of interest. We did not perform quantifications e.g. in aortic root or in ascending aorta because these MR acquisitions are non triggered and there is too much motion. Signal-to-Noise ratio SNR is highly affected by acquisition and reconstruction parameters. TWIST data were acquired with a PAT resulting in a spatially dependent SNR penalty due to g-factor based noise enhancement [33]. The noise behaviour of GRASP strongly depends on the choice of the regularization parameters in the spatial and temporal domain for the CS reconstruction [34]. Nevertheless, with a PAT factor commonly used for TWIST protocols and the standardised CS reconstruction parameters, a higher SNR was reached for the GRASP sequence (Figure 5), despite its higher spatial resolution compared to TWIST imaging. Limitations The quantitative analysis was confined to three specific regions within the aorta, which might not fully represent other areas or side branches such as aortic side branches. In addition, due to heterogeneity of the patient cohort, aortic caliber, the extent of the dissection flap in aortic dissection, or the integrity of various aortic side branches have not been assessed. Furthermore, there is no ground truth to compare with, complicating the visual assessment of certain parameters. There was some lack of agreement among readers observed (Table 4). This variance could potentially be attributed to insufficient training sessions for the radiologists involved. To mitigate this issue in future studies, it is advisable to conduct more extensive training sessions. These sessions could involve reviewing multiple images corresponding to each Likert scale point, thereby facilitating the achievement of consensus among radiologists. Moreover, enhancing the objectivity of the study could be achieved by identifying less subjective endpoints or by directly inquiring more details about the diagnostic nature of the image. By incorporating less subjective criteria or explicitly assessing diagnostic relevance, the study's subjectivity could be reduced, leading to more consistent results. Outlook The optimization of regularization parameters in the CS reconstruction [29] in the spatiotemporal domain tailored for this specific GRASP application may allow finding a better compromise between SNR, vessel sharpness, temporal blurring and streaking artefacts. The use of an improved CS type reconstruction [30] may yield in reduced undersampling artefacts. However, a systematic investigation of such recon parameters was beyond the scope of this work Conclusions In conclusion, we quantitatively and qualitatively compared the performance of a Cartesian and radial sequence for CE-trMRA on a cohort of patients with aortic diseases. GRASP outperformed TWIST offering superior image contrast, reduced image blurring to motion and benign artefact behaviour. Streaking artefacts were stronger visible on GRASP images, but did not affect diagnostics. The findings suggest that GRASP has the potential to provide reliable imaging of the aorta (or where organ motion is likely to degrade image quality) in serial follow-up, which is essential in clinical decision-making. The observations also indicate the need for more detailed investigations in the future to optimise the CS reconstruction parameters in the spatial and temporal domain, namely the undersampling factor affecting the temporal resolution as well as the regularization in spatial and temporal domain, for such CE-trMRA application. Abbreviations AVM=arteriovenous malformation CA=contrast media CE-trMRA=contrast-enhanced time-resolved magnetic resonance angiography CS=compressed sensing CTA=computed tomography angiography FoV=Field of view FWHM=full width at half maximum GA=golden angle Gd=Gadolinium-based GRASP=Golden-angle RAdial Sparse Parallel maxslope=maximum slope of the contrast agent uptake MRI=magnetic resonance Imaging PAT=parallel acquisition technique ROI=region of interest SNR=Signal-to-Noise ratio TWIST=Time-resolved angiography With Interleaved Stochastic Trajectories Declarations Funding This study was funded by the Swiss National Foundation, Sinergia CRSII5_193694 Competing Interests All authors have no conflicts of interest. Ethics approval Ethical adherence: The study was approved by the local institution review board and by the local IRB (Reference number 2022-1936). No studies involving animals were performed. 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Med Phys. 2011;38(9):5049-5057. doi: 10.1118/1.3618730. PMID: 21978049; PMCID: PMC3170395. Park J, Zhang Q, Jellus V, Simonetti O, Li D. Artifact and noise suppression in GRAPPA imaging using improved k-space coil calibration and variable density sampling. Magn Reson Med. 2005;53(1):186-93. doi: 10.1002/mrm.20328. PMID: 15690518. Feng L. Golden-Angle Radial MRI: Basics, Advances, and Applications. J MagnReson Imaging . 2022;56(1):45-62. Feng L, Wen Q, Huang C, Tong A, Liu F, Chandarana H. GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation. Magn Reson Med. 2020 Jan;83(1):94-108. Trojan, Michael et al. “Time-Resolved Three-Dimensional Contrast-Enhanced Magnetic Resonance Angiography in Patients with Chronic Expanding and Stable Aortic Dissections.” Contrast media & molecular imaging vol. 2017 5428914. 28 Nov. 2017, doi:10.1155/2017/5428914 Goldman-Yassen AE, Raz E, Borja MJ, Chen D, Derman A, Dogra S, et al. Highly time-resolved 4D MR angiography using golden-angle radial sparse parallel (GRASP) MRI. Sci Rep 12, 15099 (2022). https://doi.org/10.1038/s41598-022-18191-y. Robson PM, Grant AK, Madhuranthakam AJ, Lattanzi R, Sodickson DK, McKenzie CA. Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions. Magn Reson Med. 2008;60(4):895-907. doi: 10.1002/mrm.21728. PMID: 18816810; PMCID: PMC2838249. Velikina JV, Alexander AL, Samsonov A. Accelerating MR parameter mapping using sparsity-promoting regularization in parametric dimension. Magn Reson Med . 2013;70(5):1263-1273. doi: 10.1002/mrm.24577. Epub 2012 Dec 4. PMID: 23213053; PMCID: PMC3740070. Tables Table 1 Caption: Time-resolved contrasted-enhanced Magnetic Resonance Angiography acquisition parameters. Slice thickness, number of slices and FOV vary depending of patient’ characteristics and are equal for the same patient for the two sequences. Spatial resolution is the acquired one. GRASP TWIST Temporal resolution [s] 1.8 1.98 Spatial resolution [mm 2 ] 1.56 × 1.56 2.89 × 1.56 Slice thickness [mm] 2.5-3.5 2.5-3.5 Field Of View (FOV) [mm 2 ] 400X400-500X500 400X400-500X500 Number of slices 52-56 52-56 Undersampling factor 19.1 - Parallel Acquisition Technique (PAT) - 2 Flip angle [°] 17 17 Matrix size 256 × 256 256 × 256 Echo Time (TE) [ms] 1.19 0.96 Repetition Time (TR) [ms] 2.6 2.04 Scan time [s] 107 213 Table 2 Caption: Qualitative results. Mean and standard deviation of image quality scores among the two sequences under analysis. 4-point Likert scale used: 1=excellent; 2=good; 3=poor; 4=non-diagnostic. Results are presented as mean±standard deviation. TWIST GRASP Reader 1 Reader 2 Reader 3 Reader 1 Reader 2 Reader 3 Vessel sharpness 1.9±0.6 1.6±0.6 2.0±0.3 1.4±0.6 1.2±0.4 1.3±0.6 Vascular contrast 1.6±0.5 1.9±0.4 1.1±0.4 1.5±0.5 1.4±0.5 1.0±0.2 Image artefacts 2.2±0.6 1.9±0.3 2.0±0.5 2.6±0.6 2.3±0.5 2.8±0.6 Overall image quality 1.9±0.4 1.8±0.2 1.7±0.4 1.8±0.2 1.6±0.2 1.7±0.3 Table 3 Caption: Results of the multilevel mixed-effect proportional-odds cumulative logit models. The estimates represents the change in the log odds of moving from one level to the next level of the ordinal response variable for a one-unit increase in the predictor variable, holding all other variables constant. p-value<0.05 indicates statistical significance between the two sequences. Estimates p-value Vessel sharpness -2.3464 p<0.05 Vascular contrast -1.5284 p<0.05 Image artefacts 2.7399 p<0.05 Image quality 0.2735 p=0.05 Table 4: Caption: Inter-rater qualitative results. Comparison of the image quality scores among readers through Fleiss’ Kappa. p-value<0.05 indicates that the agreement between raters is significantly better than what would be expected by chance. Fleiss’kappa = 1: perfect agreement beyond chance; Fleiss’kappa = 0: agreement equal to what would be expected by chance alone; Fleiss’kappa < 0: agreement worse than chance; Fleiss’kappa < 0: agreement worse than chance; Fleiss’kappa p-value TWIST GRASP TWIST GRASP Vessel sharpness -0.18 0.03 0.08 0.70 Vascular contrast -0.15 0.01 0.13 0.91 Image artefacts 0.16 0.10 0.05 0.32 Overall image quality 0.00 -0.01 0.97 0.24 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Oct, 2024 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviews received at journal 05 Jun, 2024 Reviewers agreed at journal 16 May, 2024 Reviewers invited by journal 28 Apr, 2024 Editor assigned by journal 24 Apr, 2024 Submission checks completed at journal 24 Apr, 2024 First submitted to journal 22 Apr, 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. 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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-4306592","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312362694,"identity":"7d062c69-c9aa-4074-b19a-bdabcb770e61","order_by":0,"name":"Camilla Calastra","email":"data:image/png;base64,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","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":true,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Calastra","suffix":""},{"id":312362695,"identity":"5d64432e-1772-49a0-9a2c-264cc3b5e551","order_by":1,"name":"Elena Kleban","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Kleban","suffix":""},{"id":312362696,"identity":"2352ffa4-48c5-4bbc-9181-e2a934674d09","order_by":2,"name":"Fabrice Helfenstein","email":"","orcid":"","institution":"Swiss Cardiovascular Center, Inselspital, Bern University Hospital, Bern, Switzerland","correspondingAuthor":false,"prefix":"","firstName":"Fabrice","middleName":"","lastName":"Helfenstein","suffix":""},{"id":312362697,"identity":"e17e66b9-8c1a-476d-bd64-a425720dcf12","order_by":3,"name":"Fabian Haupt","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Haupt","suffix":""},{"id":312362698,"identity":"69303364-d558-435a-9375-9363b165ef05","order_by":4,"name":"Alan Arthur Peters","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"Arthur","lastName":"Peters","suffix":""},{"id":312362699,"identity":"72d655b8-3415-4a34-8f96-a6846e07574f","order_by":5,"name":"Adrian Huber","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"","lastName":"Huber","suffix":""},{"id":312362700,"identity":"f7f30a1d-1d65-45c1-92d2-8283a8c07eb1","order_by":6,"name":"Hendrik von Tengg-Kobligk","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Hendrik","middleName":"","lastName":"von Tengg-Kobligk","suffix":""},{"id":312362703,"identity":"35473e45-2469-405c-aa20-e7ee8adb96be","order_by":7,"name":"Bernd Jung","email":"","orcid":"","institution":"Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Bernd","middleName":"","lastName":"Jung","suffix":""}],"badges":[],"createdAt":"2024-04-22 14:37:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4306592/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4306592/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10554-024-03259-9","type":"published","date":"2024-10-12T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58229141,"identity":"9504a382-e601-4e03-9488-16656ea69168","added_by":"auto","created_at":"2024-06-12 19:14:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":698287,"visible":true,"origin":"","legend":"\u003cp\u003eOn the bottom, example of placement of ROIs at different levels of aorta: ascending aorta at the level of the pulmonary trunk, AA (red), descending aorta at the level of the pulmonary trunk, DA (blu), and abdominal aorta at the level of the renal arteries, AbA (green). On the top, plot of their signal intensity over time\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/f8aebb09b3d98a1231540ac0.png"},{"id":58230784,"identity":"fa581b81-4041-4ac8-962e-b4e502a67e48","added_by":"auto","created_at":"2024-06-12 19:22:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":907302,"visible":true,"origin":"","legend":"\u003cp\u003eIn A an example of line drawing perpendicular to the vessel, across descending aorta at the level of the pulmonary trunk, for GRASP is shown. In B there is the generation of profile assessment of vessel sharpness (vs) from such line and a comparison with TWIST. Normalized signal intensity is represented for an easier comparison between TWIST and GRASP even if absolute signal intensity was employed for the calculus of vs. In C there is the formula used to calculate vessel sharpness from the information obtained in B\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/6ccf6d1dfeee7b579cf1dcc5.png"},{"id":58229143,"identity":"b505cca5-6d7d-4ed0-9d44-9db59b6b5294","added_by":"auto","created_at":"2024-06-12 19:14:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":992263,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of artefacts level in three patients on GRASP. On the top line images from three patients of the study show ascending aorta being affected by streaking artefacts, in an increasing amount from left to right. On the bottom line images from the same patients show descending aorta being affected by interference artefacts. The level of such artefacts is comparable among patients and it is confined to the periphery of the FoV. For all images, the slice with maximum enhancement in time is shown\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/b041dd44ee6ae0c40b16a3be.png"},{"id":58229142,"identity":"a8ac6a91-4d04-4cac-b9bf-79651728f721","added_by":"auto","created_at":"2024-06-12 19:14:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1301524,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative image comparison between GRASP (left) and TWIST (right). FoV=400X400. GRASP images are characterised by more pronounced artefacts and by an improved vascular contrast and vessel sharpness compared to TWIST\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/1fe56f741c130fc633865f91.png"},{"id":58229140,"identity":"84934299-b473-4b38-9d85-74bb31fc8a31","added_by":"auto","created_at":"2024-06-12 19:14:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":345768,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative results at ascending aorta (AA), descending aorta (DA) at the level of the pulmonary trunk and abdominal aorta at the level of the renal arteries (AbA) for GRASP and TWIST. The star means a significant statistical difference between the two sequences\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/d8e5baa39103014a23e7e309.png"},{"id":66597143,"identity":"c2a4a77c-e5b5-47b0-b6e2-4be203d30e79","added_by":"auto","created_at":"2024-10-14 16:07:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5754243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4306592/v1/2c3e5162-3ab9-4b20-994d-cb0c3f32eeb4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Contrast-Enhanced MRA of the aorta using a Golden-Angle Radial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved Cartesian MRA (TWIST)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGadolinium-based (Gd) contrast-enhanced time-resolved magnetic resonance angiography (CE-trMRA) techniques are able to depict the anatomy and haemodynamics of complex vascular structures [1-8]. While computed tomography angiography (CTA) with its inherent ionising radiation dose remains the reference method for thoracic imaging in an acute clinical setting [1, 9-12],\u0026nbsp;CE-trMRA stands as a valuable alternative to CTA avoiding ionizing radiation and providing dynamic contrast information\u0026nbsp;[1-3, 5, 8].\u0026nbsp;Within aortic disease populations, its application holds substantial promise for advancing clinical care by delivering real-time insights into blood flow patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond its diagnostic capabilities, CE-trMRA emerges as a tool for monitoring disease progression over time [2-3]. This is invaluable for making informed decisions about the timing and necessity of interventions. Furthermore, CE-trMRA plays a pivotal role in treatment planning [1,5]. This facilitates more targeted and effective treatment strategies, enhancing overall patient care. In particular, the real-time assessment of aortic caliber, aortic aneurysms and various aortic side branches prove to be of paramount importance for diagnostic accuracy and to guide interventions with a focus on individual patient needs [4].\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe TWIST (Time-resolved angiography With Interleaved Stochastic Trajectories) sequence is a commonly used acquisition technique to perform CE-trMRA measurements. It is based on a Cartesian acquisition with sharing of k-space data between adjacent time frames to allow for a good compromise between spatial and temporal resolution [13]. The drawback of a Cartesian trajectory is its susceptibility to image artefacts in presence of respiratory motion, such as spatial blurring of vascular boundaries [14].\u003c/p\u003e\n\u003cp\u003eAcquisition techniques based on radial sampling are less sensitive to motion than those based on Cartesian sampling and consequently improve the overall image quality in presence of motion [15]. Moreover,\u0026nbsp;undersampling artifacts with radial imaging manifest as streaks, whereas they appear as aliasing with Cartesian. In contrast to Cartesian imaging, artefacts appear as incoherent aliasing in multiple dimensions [16], which is well suited for 4D image reconstruction based on compressed sensing (CS) exploiting spatial and temporal correlations and using a nonlinear reconstruction to enforce sparsity in a suitable transform domain [17, 18]. A uniform coverage of the k-space with high temporal incoherence can be obtained for any number of views, if the golden angle rotation is applied between successive echoes [19]. This enables dynamic imaging studies using continuous data acquisition and retrospective reconstruction of image series with arbitrary temporal resolution by grouping different numbers of consecutive echoes into temporal frames. The GRASP (Golden-angle RAdial Sparse Parallel) magnetic resonance imaging (MRI) technique combines the above mentioned aspects by using a stack-of-stars acquisition with a golden angle trajectory in combination with compressed sensing and parallel imaging reconstruction [20].\u003c/p\u003e\n\u003cp\u003ePrevious works have successfully proven that GRASP allows for a reliable and robust assessment of dynamic CE liver imaging [21, 22] at high acceleration factors. Also, GRASP\u0026nbsp;is known to provide robust imaging in situations with potential image degradation because of respiratory, cardiac or vascular motion artifacts [23].\u0026nbsp;Because of the above mentioned advantages, GRASP has a potential in improving image quality in body regions subjected to motion and for imaging of high-flow vascular pathologies like peripheral arteriovenous malformations (AVMs), that demand -high temporal resolution while maintaining a decent spatial resolution [24].\u003c/p\u003e\n\u003cp\u003eHence, the purpose of our intra-individual study is to investigate the potential advantages of GRASP over TWIST in patients with aortic diseases. To this end, a qualitative comparison including inter- and intra-observer variabilities as well as quantitative comparison between images acquired by conventional TWIST and GRASP sequences has been performed.\u003c/p\u003e"},{"header":"Materials and Methods ","content":"\u003cp\u003eIn this cross-over single-centre retrospective study, a cohort of 30 patients (60.9\u0026plusmn;16.1 yo, 7 females) with various chronic aortic diseases (aortic dissection type A: 10, aortic dissection type B: 7, aortic aneurysm: 8; status after contained aortic rupture: 3, Marfan syndrome: 1, stenosis of left subclavian artery and impact of coronary artery bypass: 1) underwent a clinical follow up routine MRI examination between July and October 2022. The standard TWIST was complemented by a GRASP sequence after the study was approved by the local institutional review board and all patients gave their written informed consent prior to the MRI examination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data was acquired on a 1.5T clinical scanner (Magnetom SolaFit, Siemens, Erlangen, Germany) equipped with a 32 channel body coil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith each CE-trMRA acquisition the same amount of Gd contrast agent (CA) (Gadovist 1.0M, Bayer, Switzerland AG, Zurich) was administered (0.075 ml/(kg bw), flow rate 4 ml/s), i.e. twice during each protocol. All images were acquired during free breathing in the oblique coronal plane. To reduce bias due contrast enhancement in the vascular systems during the second CA administration, TWIST and GRASP sequences were acquired in reverse order for half of the patients (n=15), respectively. For all examinations, there was a three minutes pause between CA administrations.Reconstruction of GRASP data has been performed inline at the scanner within about 30 s.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;All acquisition parameters are summarised in Table 1. For the TWIST sequence, 25% of the k-space center was used to reconstruct a time frame, whereas 33% of the remaining k-space periphery was sampled between each acquisition of k-space center [25]. For the GRASP sequence, 13 radial projections were used to reconstruct one time frame.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative image analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualitative analysis was performed using original non-subtracted 3D dynamic images. Three experienced radiologists (with 20 (#1), 5 (#2), and 8 (#3) years of experience) assessed the overall image quality independently. To this end, vascular contrast, vessel sharpness and image artefacts of TWIST and GRASP were assessed by grading the images on a 4-point Likert scale. In detail: for overall image quality, vascular contrast and vessel sharpness: 1=excellent; 2=acceptable (good); 3=poor (still diagnostic); 4=non-diagnostic\u003cem\u003e.\u0026nbsp;\u003c/em\u003eFor image artefacts: 1=no artefacts; 2=minor artefacts (not interfering with diagnostic content), 3=moderate artefacts (degrading diagnostic content, image still diagnostic), 4=severe artefacts (non-diagnostic image).\u003c/p\u003e\n\u003cp\u003eWhen assessing image artefacts special attention was paid to streaking artefacts for GRASP and fold-over artefacts for TWIST. For the overall image quality index, readers focused on vascular enhancement visibility and perfusion on the vessel of the ascending aorta, supra-aortic vessels, intercostal arteries, visceral branches, particularly inferior mesenteric artery, renal cortex (primary entry and additional small communication channels). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative image analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative image analysis was performed by MATLAB 9.12 (MathWorks, Natick, MA, USA) using original non-subtracted images. Circular regions-of-interest (ROIs) were placed at three aortic levels as shown in Figure 1: ascending aorta (AA), descending aorta at the level of the pulmonary trunk (DA), and abdominal aorta at the level of the infrarenal arteries (AbA). In cases of an aortic dissection the true lumen was chosen for the placement of the ROIs. When drawing circular ROIs, those slices and temporal frames were selected, where the anatomy of interest was best visible for the readers. For each patient identical ROIs at the same positions were used for both sequences. ROIs were drawn on TWIST images first for half of the patients. The temporal behaviour of the sequences was quantified by obtaining the maximum slope of the contrast agent uptake c (maxslope) and the full width at half maximum (FWHM) from the normalised and interpolated signal intensity time courses within the ROIs\u003cstrong\u003e.\u003c/strong\u003e Time signal intensity curves were interpolated by a factor of 10 and a smoothing function was applied to exclude the influence of noise on the outcome.\u003c/p\u003e\n\u003cp\u003eSpatial blurring was quantified by calculating vessel sharpness as follows: at the same levels of the aorta as described above a straight line perpendicular to the vessel wall was drawn across the aorta. Vessel sharpness (\u003cem\u003evs)\u0026nbsp;\u003c/em\u003eof the boundary was then calculated from the resulting signal intensity profile as vs=1/d. The value d (millimetres) is the distance between those points on the drawn line, between which the signal intensity changed from 20% to 80% of the absolute intensity difference, i.e. the difference between the maximal and the minimal signal intensity values [26] (Figure 2).\u003c/p\u003e\n\u003cp\u003eSignal-to-Noise ratio (SNR) calculation was performed by adopting the first method described in the previous work [27]. For noise, images from the second and the third time frames (prior to the bolus onset) were subtracted. The first time frame was not used as it has slightly different acquisition parameters for TWIST..\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean and standard deviation values were calculated for all outcomes of the qualitative and quantitative assessment. Statistical analysis was performed using R, version R 4.2.2 (R Foundation for Statistical Computing, 2021) and Excel, version 16.66.1 (22101101), (Microsoft, Redmond, Washington, USA). For all tests, the statistically significant difference is set to p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eMultilevel mixed-effect proportional-odds models were used for the qualitative analysis. Multilevel mixed-effect proportional-odds models include the scores as an ordinal dependent variable, the sequence (GRASP vs. TWIST) as the fixed factor and patient pseudo-ID and reader ID as random factors. The link function is logit (proportional odds).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResults of the qualitative assessment are summarised in Table 2, 3 and 4. Table 2 reports the mean value and standard deviation for each qualitative outcome for each reader., Table 3 reports the results of the multilevel mixed-effect proportional odds cumulative logic models. GRASP sequence is better than TWIST for vascular contrast and vessel sharpness (negative model estimates; scores are lower with GRASP than with TWIST; all p \u0026lt; 0.001). TWIST is superior than TWIST (positive model estimates; scores are higher with GRASP than with TWIST) for image artifacts (p \u0026lt; 0.001). Even if severe image artefacts are characteristic for the data acquired using GRASP sequence, these do not influence the diagnostics as corroborated by the image quality index (p = 0.35). Table 4 reports the results of inter-reader analysis: readers\u0026rsquo; agreement is random.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Figure 1 shows the evolution of signal intensity values for the ascending aorta at the level of the pulmonary trunk (AA, red curves), the descending aorta at the levels of pulmonary trunk (DA, blue curves) and the abdominal aorta at the level of infrarenal arteries (AbA, green curves) for GRASP and TWIST. The signal intensity values obtained with the GRASP sequence are higher than those obtained with the TWIST sequence. In addition, the corresponding time-courses are smoother. Figure 3 shows the varying amount of streaking artefacts for GRASP sequence at different levels of aorta.\u003c/p\u003e\n\u003cp\u003eRepresentative images acquired with GRASP and TWIST sequences are reported in Figure 4.\u0026nbsp;As for artefacts observed in GRASP sequence, readers observed streaking artefacts in GRASP sequence and blurring of sharp contrast or object edges in TWIST sequence. Furthermore, readers observed a combination of breathing and GRAPPA artefacts.\u0026nbsp;\u003cbr\u003e\u0026nbsp;In Figure 5 quantitative results are summarised. GRASP sequence achieved superior values in vessel sharpness and SNR. FWHM was better on images produced with the TWIST sequence and there are three outliers for both sequences. Maximum slope of the CA uptake was similar for both sequences.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe intra-individual study focused on the evaluation of a golden angle (GA) radial CE-trMRA sequence as an alternative for the conventional Cartesian sequence for imaging the aorta in patients with aortic diseases. Our results suggest that the GRASP sequence improved image quality of the aorta despite the presence of streaking artifacts (with its current temporal resolution), which is in agreement with a previous study [18]. Shown by the quantification of vessel sharpness and SNR, the depiction of the aorta is superior to conventional TWIST imaging, also corroborated by the scoring results of the experienced radiologists. A very moderate low-pass filtering in the temporal domain across the areas of interest has been observed for GRASP, whereas the uptake of CA described by the maximum slope is comparable in the two sequences. To our knowledge there is no published study describing GRASP applied to large vessels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging artefacts\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe artefact level is significantly higher in the GRASP images (Table 2, 3 and\u0026nbsp;Figure\u0026nbsp;3). This is caused by streaking artefacts as a result of high level of undersampling [18]. Using a higher number of radial projections may reduce the undersampling factor and mitigate the streaking artefacts at a cost of lower temporal resolution. Therefore, adapting the number of radial projections for the required temporal resolution for each applications is beneficial for image quality; e.g. a somewhat lower temporal resolution may be sufficient for aortic applications while resulting in a lower level of streaking artefacts. Streaking\u0026nbsp;artefacts are more pronounced in the periphery of the FoV. Within the region of interest, the ascending aorta and the aortic arch are most affected.\u003c/p\u003e\n\u003cp\u003eAs for artefacts observed in TWIST sequence, blurring of sharp contrast or object edges is due to the lower resolution in phase encoding direction and its Cartesian nature. The breathing and GRAPPA artefacts on TWIST are likely due to coil sensitivity changes during breathing [28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial appearance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImproved vessel sharpness, both qualitative and quantitative (Table 2 and 3 and Figure 5), for all ROIs locations in GRASP compared to TWIST is assumed to be a result of the higher in-plane resolution and the reduced sensitivity of the radial trajectory to breathing motion. In addition, image sharpness of the GRASP sequence is influenced by the choice of density compensation function for the balance of the k-space energy content and by the spatial weight in the CS reconstruction [29,30]. However, this cannot be controlled due to a standardised vendor reconstruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal \u0026nbsp;behaviour of the contrast enhancement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the temporal behaviour of both sequences revealed a significantly lower FWHM of the first-pass bolus for TWIST in all locations compared to GRASP (Figure 5), despite the higher temporal resolution for GRASP and the shared view of TWIST that can generate temporal blurring [31]. This may be a result of the shorter acquisition of the k-space center (scan time for the 25% of k-space center used to reconstruct one time frame ~1s) compared to GRASP, where k-space center crossing spokes (during the temporal resolution of 1.8s) were used to reconstruct a time frame. Furthermore, the CS reconstruction of GRASP sequence includes a temporal regularization term, which may introduce temporal blurring. However, it turned out by the qualitative analysis of vascular contrast (Table 2 and Table 3) that the observed differences in the dynamics are not of clinical relevance. Interestingly, two outliers for the FWHM values at all aorta locations are observed. A more detailed assessment of these data revealed abnormal flow direction in these patient due to the compressed true lumen of the aorta. No significant differences between sequences was found for the maximum upslope (Figure 5) of the first-pass bolus.\u003cbr\u003e\u0026nbsp;Additionally, the results of FWHM and max slope may not have direct clinical relevance but corroborate some clinical observations and might be relevant for quantification of some aspects [32] and hence could be useful for clinicians. e.g. quantification of aspects of vascular malformations would be of interest. We did not perform quantifications e.g. in aortic root or in ascending aorta because these MR acquisitions are non triggered and there is too much motion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignal-to-Noise ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNR is highly affected by acquisition and reconstruction parameters. TWIST data were acquired with a PAT resulting in a spatially dependent SNR penalty due to g-factor based noise enhancement [33]. The noise behaviour of GRASP strongly depends on the choice of the regularization parameters in the spatial and temporal domain for the CS reconstruction [34]. Nevertheless, with a PAT factor commonly used for TWIST protocols and the standardised CS reconstruction parameters, a higher SNR was reached for the GRASP sequence (Figure 5), despite its higher spatial resolution compared to TWIST imaging.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative analysis was confined to three specific regions within the aorta, which might not fully represent other areas or side branches such as aortic side branches.\u0026nbsp;\u003cbr\u003e\u0026nbsp;In addition, due to heterogeneity of the patient cohort, aortic caliber, the extent of the dissection flap in aortic dissection, or the integrity of various aortic side branches have not been assessed. Furthermore, there is no ground truth to compare with, complicating the visual assessment of certain parameters.\u003cbr\u003e\u0026nbsp;There was some lack of agreement among readers observed (Table 4). This variance could potentially be attributed to insufficient training sessions for the radiologists involved. To mitigate this issue in future studies, it is advisable to conduct more extensive training sessions. These sessions could involve reviewing multiple images corresponding to each Likert scale point, thereby facilitating the achievement of consensus among radiologists. Moreover, enhancing the objectivity of the study could be achieved by identifying less subjective endpoints or by directly inquiring more details about the diagnostic nature of the image. By incorporating less subjective criteria or explicitly assessing diagnostic relevance, the study\u0026apos;s subjectivity could be reduced, leading to more consistent results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutlook\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimization of regularization parameters in the CS reconstruction [29] in the spatiotemporal domain tailored for this specific GRASP application may allow finding a better compromise between SNR, vessel sharpness, temporal blurring and streaking artefacts. The use of an improved CS type reconstruction [30] may yield in reduced undersampling artefacts. However, a systematic investigation of such recon parameters was beyond the scope of this work\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we quantitatively and qualitatively compared the performance of a Cartesian and radial sequence for CE-trMRA on a cohort of patients with aortic diseases. GRASP outperformed TWIST offering superior image contrast, reduced image blurring to motion and benign artefact behaviour. Streaking artefacts were stronger visible on GRASP images, but did not affect diagnostics. The findings suggest that GRASP has the potential to provide reliable imaging of the aorta (or where organ motion is likely to degrade image quality) in serial follow-up, which is essential in clinical decision-making. The observations also indicate the need for more detailed investigations in the future to optimise the CS reconstruction parameters in the spatial and temporal domain, namely the undersampling factor affecting the temporal resolution as well as the regularization in spatial and temporal domain, for such CE-trMRA application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAVM=arteriovenous malformation\u0026nbsp;\u003cbr\u003e\u0026nbsp;CA=contrast media\u003cbr\u003e\u0026nbsp;CE-trMRA=contrast-enhanced time-resolved magnetic resonance angiography\u0026nbsp;\u003cstrong\u003e\u003cu\u003e\u003cbr\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003eCS=compressed sensing\u003cstrong\u003e\u003cu\u003e\u003cbr\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003eCTA=computed tomography angiography\u003cbr\u003e\u0026nbsp;FoV=Field of view\u0026nbsp;\u003cbr\u003e\u0026nbsp;FWHM=full width at half maximum\u003cbr\u003e\u0026nbsp;GA=golden angle\u003cbr\u003e\u0026nbsp;Gd=Gadolinium-based\u003cbr\u003e\u0026nbsp;GRASP=Golden-angle RAdial Sparse Parallel\u003cbr\u003e\u0026nbsp;maxslope=maximum slope of the contrast agent uptake\u0026nbsp;\u003cbr\u003e\u0026nbsp;MRI=magnetic resonance Imaging\u003cbr\u003e\u0026nbsp;PAT=parallel acquisition technique\u0026nbsp;\u003cbr\u003e\u0026nbsp;ROI=region of interest\u003cbr\u003e\u0026nbsp;SNR=Signal-to-Noise ratio\u003cbr\u003e\u0026nbsp;TWIST=Time-resolved angiography With Interleaved Stochastic Trajectories\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Swiss National Foundation, Sinergia CRSII5_193694\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical adherence: The study was approved by the local institution review board and by the local IRB (Reference number 2022-1936). No studies involving animals were performed. Written informed consent was obtained from all subjects according to our institutional guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu Q, Lu JP, Wang F, Wang L, Tian JM. Three-dimensional contrast-enhanced MR angiography of aortic dissection: a pictorial essay. Radiographics. 2007;27(5):1311-1321. doi: 10.1148/rg.275065737. PMID: 17848693.\u003c/li\u003e\n\u003cli\u003eRengier F, Geisb\u0026uuml;sch P, Vosshenrich R, M\u0026uuml;ller-Eschner M, Karmonik C, Schoenhagen P, et al. State-of-the-art aortic imaging: part I - fundamentals and perspectives of CT and MRI. \u003cem\u003eVasa\u003c/em\u003e. 2013;42(6):395-412. doi: 10.1024/0301-1526/a000309. PMID: 24220116.\u003c/li\u003e\n\u003cli\u003eCzerny M, Schmidli J, Vosshenrich R, van den Berg JC, Bertoglio L, Carrel T, et al. 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PMID: 26888549.\u003c/li\u003e\n\u003cli\u003eRapacchi, S., Natsuaki, Y., Plotnik, A., Gabriel, S., Laub, G., Finn, et al. Reducing view-sharing using compressed sensing in time-resolved contrast-enhanced magnetic resonance angiography. Magn. Reson. Med. 2015; 74:474-481. doi: 10.1002/mrm.25414. Epub 2014 Aug 26. PMID: 25157749.\u003c/li\u003e\n\u003cli\u003eBarger AV, Grist TM, Block WF, Mistretta CA. Single breath-hold 3D contrast-enhanced method for assessment of cardiac function. Magn. Reson. Med.2000;44: 821-824. doi: 10.1002/1522-2594(200012)44:6\u0026lt;821::aid-mrm1\u0026gt;3.0.co;2-s. PMID: 11108617.\u003c/li\u003e\n\u003cli\u003eBlock KT, Uecker M, Frahm J. Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magn Reson Med. 2007;57:1086\u0026ndash;1098. doi: 10.1002/mrm.21236. PMID: 17534903.\u003c/li\u003e\n\u003cli\u003eGamper U, Boesiger P, Kozerke S. Compressed sensing in dynamic MRI. Magn. Reson. Med. 2008;59:365-373. doi: 10.1002/mrm.21477. PMID: 18228595.\u003c/li\u003e\n\u003cli\u003eBlock KT, Chandarana H, Milla S, Bruno M, Mulholland T, Fatterpekar G et al. Towards Routine Clinical Use of Radial Stack-of-Stars 3D Gradient-Echo Sequences for Reducing Motion Sensitivity. J Korean Soc Magn Reson Med. 2014;18(2):87-106. https://doi.org/10.13104/jksmrm.2014.18.2.87\u003c/li\u003e\n\u003cli\u003eWinkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O. An optimal radial profile order based on the Golden Ratio for time-resolved MRI. IEEE Trans Med Imaging. 2007 Jan;26(1):68-76. doi: 10.1109/TMI.2006.885337. PMID: 17243585.\u003c/li\u003e\n\u003cli\u003eFeng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn. Reson. Med. 2014;3;72:707-17. doi: 10.1002/mrm.24980. Epub 2013 Oct 18. PMID: 24142845; PMCID: PMC3991777.\u003c/li\u003e\n\u003cli\u003eWeiss J, Ruff C, Grosse U, Gr\u0026ouml;zinger G, Horger M, Nikolaou K, et al. Assessment of Hepatic Perfusion Using GRASP MRI: Bringing Liver MRI on a New Level. Invest Radiol. 2019;54(12):737-743. doi: 10.1097/RLI.0000000000000586. PMID: 31206392.\u003c/li\u003e\n\u003cli\u003eChandarana H, Feng L, Block TK, Rosenkrantz AB, Lim RP, Babb JS, et al. Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling. \u003cem\u003eInvest Radiol\u003c/em\u003e. 2013;48(1):10-16. doi: 10.1097/RLI.0b013e318271869c. PMID: 23192165; PMCID: PMC3833720.\u003c/li\u003e\n\u003cli\u003ePiekarski E, Chitiboi T, Ramb R, Larry LA, Bhatla P, Feng L et al. \u0026ldquo;Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration.\u0026rdquo; \u003cem\u003eMagma (New York, N.Y.)\u003c/em\u003e vol. 31,1 (2018): 49-59. doi:10.1007/s10334-017-0655-7\u003c/li\u003e\n\u003cli\u003eHuf VI, Fellner C, Wohlgemuth WA, Stroszczynski C, Schmidt M, Forman C, et al. Fast TWIST with iterative reconstruction improves diagnostic accuracy of AVM of the hand. Sci Rep 10, 16355 (2020). https://doi.org/10.1038/s41598-020-73331-6\u003c/li\u003e\n\u003cli\u003eLim RP, Shapiro M, Wang EI, Law M, Babb JS, Rueff LE, et al. 3D time-resolved MR angiography (MRA) of the carotid arteries with time-resolved imaging with stochastic trajectories: comparison with 3D contrast-enhanced Bolus-Chase MRA and 3D time-of-flight MRA. AJNR Am J Neuroradiol. 2008 Nov;29(10):1847-54. doi: 10.3174/ajnr.A1252. Epub 2008 Sep 3. PMID: 18768727; PMCID: PMC8118944.\u003c/li\u003e\n\u003cli\u003eTachikawa Y, Hamano H, Yoshikai H, Ikeda K, Maki Y, Hirata K, et al. Three-dimensional multicontrast blood imaging with a single acquisition: Simultaneous non-contrast-enhanced MRA and vessel wall imaging in the thoracic aorta. Magn Reson Med. 2022;88(2):617-632. doi: 10.1002/mrm.29217. Epub 2022 Apr 18. PMID: 35436368.\u003c/li\u003e\n\u003cli\u003eGoerner FL, Clarke GD. Measuring signal-to-noise ratio in partially parallel imaging MRI. Med Phys. 2011;38(9):5049-5057. doi: 10.1118/1.3618730. PMID: 21978049; PMCID: PMC3170395.\u003c/li\u003e\n\u003cli\u003ePark J, Zhang Q, Jellus V, Simonetti O, Li D. Artifact and noise suppression in GRAPPA imaging using improved k-space coil calibration and variable density sampling. Magn Reson Med. 2005;53(1):186-93. doi: 10.1002/mrm.20328. PMID: 15690518.\u003c/li\u003e\n\u003cli\u003eFeng L. Golden-Angle Radial MRI: Basics, Advances, and Applications. \u003cem\u003eJ MagnReson Imaging\u003c/em\u003e. 2022;56(1):45-62.\u003c/li\u003e\n\u003cli\u003eFeng L, Wen Q, Huang C, Tong A, Liu F, Chandarana H. GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation. \u003cem\u003eMagn Reson Med. 2020\u003c/em\u003e Jan;83(1):94-108.\u003c/li\u003e\n\u003cli\u003eTrojan, Michael et al. \u0026ldquo;Time-Resolved Three-Dimensional Contrast-Enhanced Magnetic Resonance Angiography in Patients with Chronic Expanding and Stable Aortic Dissections.\u0026rdquo; \u003cem\u003eContrast media \u0026amp; molecular imaging\u003c/em\u003e vol. 2017 5428914. 28 Nov. 2017, doi:10.1155/2017/5428914\u003c/li\u003e\n\u003cli\u003eGoldman-Yassen AE, Raz E, Borja MJ, Chen D, Derman A, Dogra S, et al. Highly time-resolved 4D MR angiography using golden-angle radial sparse parallel (GRASP) MRI. Sci Rep 12, 15099 (2022). https://doi.org/10.1038/s41598-022-18191-y.\u003c/li\u003e\n\u003cli\u003eRobson PM, Grant AK, Madhuranthakam AJ, Lattanzi R, Sodickson DK, McKenzie CA. Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions. Magn Reson Med. 2008;60(4):895-907. doi: 10.1002/mrm.21728. PMID: 18816810; PMCID: PMC2838249.\u003c/li\u003e\n\u003cli\u003eVelikina JV, Alexander AL, Samsonov A. Accelerating MR parameter mapping using sparsity-promoting regularization in parametric dimension. \u003cem\u003eMagn Reson Med\u003c/em\u003e. 2013;70(5):1263-1273. doi: 10.1002/mrm.24577. Epub 2012 Dec 4. PMID: 23213053; PMCID: PMC3740070.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cem\u003eTable 1\u003c/em\u003e\u0026nbsp; \u0026nbsp;\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eTime-resolved contrasted-enhanced Magnetic Resonance Angiography acquisition parameters. Slice thickness, number of slices and FOV vary depending of patient\u0026rsquo; characteristics and are equal for the same patient for the two sequences. Spatial resolution is the acquired one.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eTemporal resolution [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eSpatial resolution [mm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e1.56 \u0026times; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e2.89 \u0026times; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eSlice thickness [mm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e2.5-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e2.5-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eField Of View (FOV)\u0026nbsp;[mm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e400X400-500X500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e400X400-500X500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of slices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e52-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e52-56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eUndersampling factor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eParallel Acquisition Technique (PAT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eFlip angle [\u0026deg;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eMatrix size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e256\u0026nbsp;\u0026times;\u0026nbsp;256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e256\u0026nbsp;\u0026times;\u0026nbsp;256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eEcho Time (TE) [ms]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eRepetition Time (TR) [ms]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.189735614307935%\" valign=\"top\"\u003e\n \u003cp\u003eScan time [s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.438569206842924%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.371695178849144%\" valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eTable 2\u0026nbsp;\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eQualitative results. Mean and standard deviation of image quality scores among the two sequences under analysis. 4-point Likert scale used: 1=excellent; 2=good; 3=poor; 4=non-diagnostic. Results are presented as mean\u0026plusmn;standard deviation.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.487603305785125%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"38.67768595041322%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.83471074380165%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45214521452145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036303630363037%\" valign=\"top\"\u003e\n \u003cp\u003eReader 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.366336633663366%\" valign=\"top\"\u003e\n \u003cp\u003eReader 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45214521452145%\" valign=\"top\"\u003e\n \u003cp\u003eVessel sharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e1.9\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036303630363037%\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.4\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.366336633663366%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45214521452145%\" valign=\"top\"\u003e\n \u003cp\u003eVascular contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.9\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036303630363037%\" valign=\"top\"\u003e\n \u003cp\u003e1.1\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.4\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.366336633663366%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45214521452145%\" valign=\"top\"\u003e\n \u003cp\u003eImage artefacts\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.9\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036303630363037%\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e2.6\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e2.3\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.366336633663366%\" valign=\"top\"\u003e\n \u003cp\u003e2.8\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45214521452145%\" valign=\"top\"\u003e\n \u003cp\u003eOverall image quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e1.9\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036303630363037%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.201320132013201%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.366336633663366%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eTable 3\u0026nbsp;\u003c/em\u003e\u003cem\u003eCaption:\u0026nbsp;\u003c/em\u003eResults of the multilevel mixed-effect proportional-odds cumulative logit models. The estimates represents the change in the log odds of moving from one level to the next level of the ordinal response variable for a one-unit increase in the predictor variable, holding all other variables constant. p-value\u0026lt;0.05 indicates statistical significance between the two sequences.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993527508090615%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"36.08414239482201%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.922330097087375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993527508090615%\" valign=\"top\"\u003e\n \u003cp\u003eVessel sharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08414239482201%\" valign=\"top\"\u003e\n \u003cp\u003e-2.3464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.922330097087375%\" valign=\"top\"\u003e\n \u003cp\u003ep\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993527508090615%\" valign=\"top\"\u003e\n \u003cp\u003eVascular contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08414239482201%\" valign=\"top\"\u003e\n \u003cp\u003e-1.5284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.922330097087375%\" valign=\"top\"\u003e\n \u003cp\u003ep\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993527508090615%\" valign=\"top\"\u003e\n \u003cp\u003eImage artefacts\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08414239482201%\" valign=\"top\"\u003e\n \u003cp\u003e2.7399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.922330097087375%\" valign=\"top\"\u003e\n \u003cp\u003ep\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993527508090615%\" valign=\"top\"\u003e\n \u003cp\u003eImage quality\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.08414239482201%\" valign=\"top\"\u003e\n \u003cp\u003e0.2735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.922330097087375%\" valign=\"top\"\u003e\n \u003cp\u003ep=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eTable 4:\u0026nbsp;\u003c/em\u003e\u003cem\u003eCaption: \u0026nbsp;\u003c/em\u003eInter-rater qualitative results. Comparison of the image quality scores among readers through Fleiss\u0026rsquo; Kappa. p-value\u0026lt;0.05 indicates that the agreement between raters is significantly better than what would be expected by chance. Fleiss\u0026rsquo;kappa = 1: perfect agreement beyond chance; Fleiss\u0026rsquo;kappa = 0: agreement equal to what would be expected by chance alone; Fleiss\u0026rsquo;kappa \u0026lt; 0: agreement worse than chance; \u003cstrong\u003eFleiss\u0026rsquo;kappa\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026lt; 0:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;agreement worse than chance;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.881619937694705%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"36.137071651090345%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFleiss\u0026rsquo;kappa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.981308411214954%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.92511700468019%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.252730109204368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.096723868954758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940717628705148%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.92511700468019%\" valign=\"top\"\u003e\n \u003cp\u003eVessel sharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.252730109204368%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.096723868954758%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940717628705148%\" valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.92511700468019%\" valign=\"top\"\u003e\n \u003cp\u003eVascular contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.252730109204368%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.096723868954758%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940717628705148%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.92511700468019%\" valign=\"top\"\u003e\n \u003cp\u003eImage artefacts\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.252730109204368%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.096723868954758%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940717628705148%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.92511700468019%\" valign=\"top\"\u003e\n \u003cp\u003eOverall image quality\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.252730109204368%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.096723868954758%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940717628705148%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"contrast enhanced, time resolved, MRA, thoracic imaging, aortic diseases, radial trajectory, GRASP","lastPublishedDoi":"10.21203/rs.3.rs-4306592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4306592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePurpose: \u003c/em\u003eto compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic diseases patient cohort: the conventional Cartesian-sampling-based, TWIST sequence, and the radial-sampling-based GRASP sequence. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and consequently are expected to improve the image quality in body parts subjected to motion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods: \u003c/em\u003e1.5T magnetic resonance angiography data from thirty patients (60.9±16.1y.o.) were assessed to investigate image quality as well as spatial and temporal blurring in the ascending aorta (AA), descending aorta (DA) and abdominal aorta (AbA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults: \u003c/em\u003eGRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis (TWIST, reader 1: 1.6±0.5; reader 2: 1.9±0.4; reader 3: 1.1±0.4; GRASP,\u0026nbsp; reader 1: 1.5±0.5; reader 2: 1.4±0.5; reader 3: 1.0±0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9±0.6; reader 2: 1.6±0.6; reader 3: 2.0±0.3; GRASP,\u0026nbsp; reader 1: 1.4±0.6; reader 2: 1.2±0.4; reader 3: 1.3±0.6) and quantitative analysis (TWIST, AA=0.12±0.04, DA=0.12±0.03, AbA=0.11±0.03; GRASP, AA=0.20±0.05, DA=0.22±0.06, AbA\u003csub\u003e=\u003c/sub\u003e0.20±0.05). Streaking artefacts of GRASP were stronger visible compared to TWIST (TWIST, reader 1: 2.2±0.6; reader 2: 1.9±0.3; reader 3: 2.0±0.5; GRASP, reader 1: 2.6±0.6; reader 2: 2.3±0.5; reader 3: 2.8±0.6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion:\u003c/em\u003e GRASP outperformed TWIST in SNR, vessel sharpness and reduction in image blurring; streaking artifacts were stronger visible with GRASP, but did not affect diagnostic image quality.\u003c/p\u003e","manuscriptTitle":"Dynamic Contrast-Enhanced MRA of the aorta using a Golden-Angle Radial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved Cartesian MRA (TWIST)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 19:14:05","doi":"10.21203/rs.3.rs-4306592/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-03T10:09:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-03T02:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234746718071261734147286832372026625657","date":"2024-06-12T04:36:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T02:29:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156213450901569691892246114665087159015","date":"2024-05-16T23:43:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-28T17:05:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T04:57:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T04:57:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Cardiovascular Imaging","date":"2024-04-22T14:13:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb531ec8-7c77-400c-b604-9c6009222f5c","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:01:11+00:00","versionOfRecord":{"articleIdentity":"rs-4306592","link":"https://doi.org/10.1007/s10554-024-03259-9","journal":{"identity":"the-international-journal-of-cardiovascular-imaging","isVorOnly":false,"title":"The International Journal of Cardiovascular Imaging"},"publishedOn":"2024-10-12 15:57:29","publishedOnDateReadable":"October 12th, 2024"},"versionCreatedAt":"2024-06-12 19:14:05","video":"","vorDoi":"10.1007/s10554-024-03259-9","vorDoiUrl":"https://doi.org/10.1007/s10554-024-03259-9","workflowStages":[]},"version":"v1","identity":"rs-4306592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4306592","identity":"rs-4306592","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}