Evaluation of Cerebral blood flow dynamics in rat brain using Phase contrast MRI. Technical challenges and physiological consideration

Evaluation of Cerebral blood flow dynamics in rat brain using Phase contrast MRI. 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Technical challenges and physiological consideration Kamel Abderrahim, Sidy Fall, Olivier Baledent This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7730675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract (max 200 words): The cranium is a rigid structure that encloses the brain parenchyma, arterial and venous blood, and cerebrospinal fluid (CSF), all sharing a non-expandable space. This anatomical constraint requires a regulated balance between cerebral blood supply and CSF oscillations to maintain adequate intracranial pressure. In several brain disorders, studies have reported the involvement of these circulations and their interactions with the glymphatic network. The rat, widely used in neuroscience, shares important similarities with the human brain, making it a suitable model for investigating cerebral blood flow dynamics. However, no study has yet specifically examined the arterial and venous contributions to cerebral circulation in detail. In this study, we used phase-contrast magnetic resonance imaging (PC-MRI), the only non-invasive technique that enables a detailed assessment of both arterial inflow and venous outflow within minutes. Eight rats were anesthetized with isoflurane, and three optimized imaging planes were acquired to capture venous and arterial structures. Preliminary results showed a strong correlation between arterial inflow and venous outflow (R = 0.851, p = 0.007), indicating arterio-venous coupling and confirming the consistency of the acquired data. Considerable inter-individual variability was observed on the venous side, which may be related to physiological and the limits of the approach. these findings demonstrate that PC-MRI provides a reliable non-invasive method to assess arterio-venous interactions in the rat brain Health sciences/Anatomy Physical sciences/Engineering Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Bloodflow Cerebral blood flow Phase-contrast MRI Rat model Hemodynamics Preclinical imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction In human, The intracranial compartment is composed of three primary elements: blood, cerebrospinal fluid (CSF), and brain parenchyma. According to the Monro-Kellie doctrine, this compartment is considered incompressible, meaning that the total volume within the cranium remains constant. As such, any volumetric change in one component must be offset by a reciprocal change in one or more of the others to preserve stable intracranial pressure (ICP) 1 . In this rich and complex anatomical context, non-invasive assessment of vascular parameters—such as cerebral blood volume (CBV), cerebral blood flow (CBF), and different arterial and venous flows is crucial for characterizing both normal and pathological cerebral perfusion. Separating this two compartment may enable the detection of early changes in brain physiology. 2 While space-occupying lesions such as tumors, hematomas, or abscesses are recognized causes of ICP elevation, fluctuations in CSF or cerebral blood volumes may disrupt this equilibrium in case of Clinical conditions including hydrocephalus , venous sinus thrombosis , and idiopathic intracranial hypertension (IIH) that represent a non-mass pathologies that can disrupt intracranial compliance 3 . A comprehensive understanding of cerebral hemodynamics is essential to elucidate the physiological mechanisms that underlie cerebral perfusion. Doppler ultrasound (US) and phase-contrast (PC) MRI are the methods of choice for measuring flow-related parameters 4 . One of the main advantages of phase-contrast MRI is its high spatial resolution combined with operator-independence, which contributes to a high level of reproducibility in imaging plane placement. Unlike ultrasound, despite its high temporal resolution, it is currently not possible to access deep venous and arterial structures due to the cranium. The concept of encoding blood flow velocities using phase was first introduced by Paul R.Moran in the early 1980s. When a pair of bipolar gradients is applied, stationary spins experience no net phase shift, whereas moving spins acquire a phase shift proportional to their velocity. Spins moving at the same speed in opposite directions exhibit equal but opposite phase shifts, allowing velocity to be quantified from phase measurements 5 . Several studies demonstrated the strong relationship between venous and arterial flow notably The study conducted by Sato et al. (2017) 6 in human subjects, The authors showed that increased cerebral arterial flow—measured at the level of the internal carotid artery (ICA) and vertebral artery (VA)—is accompanied by parallel adjustments in venous drainage via the internal jugular vein (IJV) and vertebral vein (VV), with statistically significant correlations (r = 0.73 for ICA–IJV) These finding suggest the existence of a dynamic coupling between arterial inflow and venous outflow. In addition, recent studies have shown that PC-MRI can detect early alterations in CSF pulsatility, in which stroke volume may reflect changes in intracranial compliance or vascular resistance 7 . In rats one of the most widely used model organisms in basic neuroscience and brain research 8 the cerebral vascular system is highly complex, characterized by a rich architectural organization that ensures consistent and reliable blood supply. This complexity is manifested in an extensive arterial and venous network, organized around multiple anastomotic circles, notably the Circle of Willis, as well as superficial and deep venous systems. Furthermore, the dural venous sinuses contribute significantly to efficient cerebral venous drainage. From an anatomical perspective, the rat brain is supplied by two distinct vascular systems: the internal carotid system and the vertebrobasilar system. These two systems are interconnected, forming a regulatory network that adjusts blood flow dynamics in response to neuronal demands through feedback and feedforward mechanisms 9 . Nevertheless, to our knowledge, no study in rats has simultaneously examined both arterial and venous structures, even though the arteriovenous dynamics appear to be important for capturing volume changes within the brain. The assessment of flow profiles can provide valuable insights into the impact of pathological conditions on macroscopic blood flow, as well as into the behavior of the arteriovenous system in relation to changes in age, sex, body weight, and strain. 10–12 This study aims to quantify vascular cerebral inflow and outflow dynamic during cardiac cycle, of healthy Spragues dewlays rats using PCMRI. Results Arterial and Venous Flow Analysis Figure 4 illustrate the distribution of flow parameters: mean, standard deviation, and maximum values within the arterial, venous, and sinus structures, respectively. These visualizations provide a comprehensive overview of the hemodynamic characteristics observed across all studied regions. In addition, comparisons between male and female subjects were performed to assess potential sex-related differences in blood flow dynamics The statistical analysis revealed several significant differences in flow values between male and female rats across venous, sinus, and arterial compartments. In the veins (Fig. 4 . B ), significant differences were observed in the right posterior facial vein (R PFV) and left Ipets vein, with flow values reduced in females ( p < 0.001). No significant differences were found for the left facial vein (L PFV) and right Ipets vein (R Ipets). Within the dural sinuses (Fig. 4 . C) , significant sex-related differences were detected in the superior sagittal sinus (SSS), straight sinus (SS), and left transverse sinus (L TRS), indicating sex-specific vascular organization. No significant difference was observed in the right transverse sinus (R TRS). Regarding the arterial system (Fig. 4 . A) , females showed significantly lower flow in the left internal carotid artery (L ICA), external carotid arteries (L and R ECA), and both PTGAL arteries. No significant sex effect was found in the right internal carotid artery (R ICA) or the basilar artery (BA). Arterial Structure-Specific Analysis We decided to analyze both the ICA, BA and the ECA, considering that these vessels exhibit the greatest hemodynamic pattern. Figure 5 displays the blood flow profiles in the external carotid arteries (ECA), internal carotid arteries (ICA), the basilar artery (BA), and the common carotid arteries (CCA). All curves exhibit a characteristic pattern reflecting the influence of the cardiac cycle on cerebral blood flow. This pattern can be decomposed into two main phases: A systolic phase, characterized by a rapid and progressive rise in flow, typically occupying the first third of the cardiac cycle. A diastolic phase, marked by a gradual decline followed by relatively stable flow, spanning the remaining two-thirds of the cycle. The systolic peaks occur predominantly around 50 ms for the ICA and ECA, approximately 45 ms for the CCA, and around 35 ms for the basilar artery. This temporal difference may reflect distinct hemodynamic behaviors between the anterior and posterior cerebral circulation. Subject F2 shows significantly lower blood flow values in the internal carotid arteries (ICA) compared to the other subjects, with reduced values observed on both the left and right sides. Venous compartment The analysis of transverse sinus (STR) (Fig. 6 A) flow revealed that the mean value on the right side (3 ml/min) was substantially higher than on the left side (1 ml/min). The pulsatility index (PI) was also higher on the right (0.6) compared to the left (0.32). This difference between hemispheres was statistically significant ( p = 0.0116). For the posterior frontal vein (PFV)(Fig. 6 B), the mean flow values were 9.1615 ml/min (right) and 6.5308 ml/min (left). No statistically significant difference was observed between hemispheres, with PI values of 0.7 (right) and 0.6 (left). Net Flow & arteriovenous coupling To characterize the overall cerebral blood flow dynamics, we analyzed both net arteriovenous flow and the relationship between arterial inflow and venous outflow. As shown in Fig. 7 A, the arteriovenous net flow curve, obtained by summing arterial inflow and venous outflow, exhibite a clear pulsatile pattern over the cardiac cycle, peaking around 50 ms with values close to + 20 ml/min this reflects the instantaneous balance between blood entering and leaving the brain, highlighting the pulsatile dynamics of cerebral circulation over the cardiac cycle. In addition, As illustrated in Fig. 7 B, correlation analysis between mean inflow and mean outflow rates across subjects revealed a strong positive linear relationship (R = 0.851, p = 0.007). This indicates that higher inflow values are consistently matched by higher outflow, with the regression line confirming a robust proportionality between arterial inflow and venous outflow within the measured range. Discussion This study demonstrates the feasibility of non-invasive mapping of cerebral hemodynamics in rodents using a 7 Tesla MRI system with prospective cardiac triggering. By implementing optimized 2D phase-contrast imaging planes, we successfully quantified both arterial inflow and venous outflow across multiple vascular compartments in Sprague-Dawley rats. A key strength of this approach lies in its capacity to assess multiple vascular segments within a single acquisition plan, enabling coherent and comparative analysis of dynamic blood flow. Our results revealed a consistent inflow pattern through the internal carotid arteries (ICA), characterized by high pulsatile pattern, which is indicative of strong arterial waveforms and potentially increased vascular stiffness. In contrast, venous outflow showed greater inter-individual variability. This variability may reflect individual differences in venous architecture and collateral pathways. The observed asymmetry between left and right transverse sinuses further reinforces this variability and may point to subject-specific anatomical differences or functional disparities in venous drainage routes. A simultaneous acquisition strategy for both arterial and venous structures was implemented through tailored slice positioning. This enabled real-time evaluation of arteriovenous dynamics, providing insight into cerebrovascular compliance. Interestingly, a direct and stable correlation was observed between mean arterial inflow and measured venous outflow. This relationship likely reflects the accuracy of PC MRI. Our cohort exhibited inter-individual variability in mean cerebral blood flow. Subjects with lower arterial inflow demonstrated correspondingly lower cerebral blood flow, whereas higher arterial inflow was consistently associated with higher cerebral blood flow. This relationship was further confirmed by a strong positive linear correlation between mean inflow and outflow rates across subjects (R = 0.851, p = 0.007). The regression analysis highlights a proportional balance between arterial inflow and venous outflow consistent with the physiological principle of flow conservation. Residual variability may reflect inter-individual anatomical differences, measurement noise, or contributions from collateral vascular pathways not directly captured in the primary inflow–outflow assessment. As we have shown, inter-individual variability of CBF in rats is significant, similar to what is observed in humans 15 . This variability is expected to affect all MRI sequences that quantify cerebral blood perfusion, such as ASL. In this context, CBF quantified by PC-MRI could serve as a reference Hypertension and neurodegenerative diseases such as Alzheimer’s disease or hydrocephalus appear to be related to cerebral vascular alterations. In such studies, PC-MRI could provide novel biomarkers based on flow dynamics and their pulsatility This preliminary work should be extended to larger populations, considering sex, age, and weight, in order to establish reference values for cerebral vascular dynamics. Improvements in the hardware should also enable the quantification of CSF oscillations, thereby complementing investigations of craniospinal neurofluids, which primarily interact with the glymphatic system. Despite these promising outcomes, several experimental and methodological limitations must be acknowledged. One of the main challenges was maintaining consistent physiological stability across animals. Heart rate and respiration in rodents are highly sensitive to body temperature 16 , and even small fluctuations can influence flow measurements. To mitigate this, the depth of anesthesia was carefully monitored, and temperature control was applied. However, maintaining strictly stable physiological conditions remains technically challenging, particularly over longer acquisitions time due to fluctuations in heart rate Additional variability came from differences in animal positioning and body size. The studied groups were not weight-matched, which led to anatomical differences influencing both image geometry and signal-to-noise ratio (SNR). This was further complicated by the use of a surface coil for signal reception, where SNR decreases with depth, potentially impacting flow measurements in deeper structures such as the basilar artery. Slice positioning also represents a critical limitation 4 . Although TOF images were used to guide slice planning, inter-individual anatomical variability and slight differences in positioning—both between and within animals—made precise reproducibility difficult. Even repeated scans of the same animal often resulted in small discrepancies due to cradle placement or minor movements. Nonetheless, overall slice positioning was sufficiently consistent to allow inter-subject comparison. Several technical aspects of acquisition required careful consideration. The number of cardiac frames was determined based on the recorded heart rate, incorporating a 10% margin to accommodate physiological variability. Proper ECG electrode placement, along with conductive gel, was essential to minimize false triggers. Spatial resolution was another important factor: while higher resolution is desirable for small vessels, such as the basilar artery, partial volume effects remain a concern 17 . Unfortunately, it was not feasible to tailor resolution for each structure individually within a single slice, especially when both large and small vessels were included. Finally, the use of a single velocity encoding (Venc) value per slice imposed another methodological constraint. In slice 1, for instance, both arteries and veins were analyzed using a Venc optimized for high-velocity arterial flow (e.g., 80 cm/s). While this setting is adequate for arteries, it may overestimate or fail to capture slower venous flows. The inability to apply dual-Venc settings within the same slice is a current technical limitation, though future protocols may integrate more adaptive encoding schemes. Methods Animal Model All animal procedures were conducted with prior approval from the French Ministry of Higher Education and Research and the local Institutional Animal Care and Use Committee (APAFIS#47673-2024022115164143 v6), in strict accordance with institutional ethical guidelines, the European Directive 2010/63/EU for the protection of animals used for scientific purposes, and the ARRIVE guidelines. A total of eight Sprague-Dawley (SD) rats were used in this study: five females (mean weight: 402 ± 35 g, age: 34 weeks) and three males (mean weight: 580 ± 20 g, age: 15 weekFor each imaging session, animals were anesthetized using 2% isoflurane in 1 L/min oxygen. Induction was carried out in a heated isoflurane chamber, ensuring uniform and stress-free anesthesia initiation. Once anesthetized, animals were positioned on a temperature-regulated water circulation pad to maintain a body temperature of ~ 37°C, and their heads were fixed in place using stereotaxic ear bars to prevent motion during scanning. Physiological monitoring including oxygen saturation, heart rate, respiratory rate, and body temperature were performed using the SA Instruments system 13 . Isoflurane concentration was adjusted as needed to maintain stable anesthetic depth. In cases of oxygen desaturation, supplemental oxygen was provided to restore physiological parameters and ensure animal welfare. Animals were kept alive for potential reuse in a future project (authorization request in preparation); when required, euthanasia was performed by CO₂ chamber in accordance with institutional and EU guidelines. Inclusion and Exclusion Criteria All animals underwent systematic visual monitoring and regular body weight measurements to ensure their overall health status. High-resolution T2-weighted MRI scans and 3D Time-of-Flight (TOF) angiographic sequences were acquired to confirm the normal cerebral anatomy of the rats. The TOF imaging further enabled detailed assessment of the vascular architecture, providing a non-invasive evaluation of the integrity and distribution of the cerebral vasculature. MRI Acquisition Protocol MRI acquisitions were performed on a 7 Tesla Bruker BioSpec 70/20 scanner (Bruker, Ettlingen, Germany) equipped with a 630 mT/m gradient insert. A volumetric coil was used for transmission, and signal reception was achieved using a 2×2-element surface array coil. Table 1 summarizes the MRI acquisition parameters used for cerebrovascular flow assessment, including phase-contrast velocity encoding (Venc) values optimized for arteries, veins, and venous sinuses. Table 1 Main PC-MRI parameters used in the study Parameter Value Repetition Time (TR) 10 ms Echo Time (TE) 3 ms Flip Angle 20° Slice Thickness 1.00 mm Field of View (FOV) 35 mm × 35 mm Pixel Size 0.1367 mm × 0.1367 mm Acquisition Matrix ≈ 256 × 256 Venc – Arteries 80 cm/s Venc (Transverse sinuses) 20 cm/s Venc (SSS & SS) 10 cm/s Acquisition Time ≈ 2 min One of the principal methodological constraints of this study was the necessity to simultaneously capture both arterial inflow and venous outflow within a single imaging plane, thereby ensuring accurate temporal synchronization and volumetric correspondence across the examined vascular structures. The primary imaging slice was carefully positioned to encompass key arterial vessels, including the internal carotid arteries (ICA), the basilar artery (BA), and the external carotid arteries (ECA), alongside major venous structures such as the inferior petrosal sinuses (Ipets) and the posterior facial veins (PFV )( Fig. 1 ). Notably, this plane also included the pterygopalatine arteries (PTgal), which—depending on inter-animal anatomical variability and subject positioning could reliably be visualized within the same slice as the ICA. It is important to emphasize that, anatomically, the ICA bifurcates into two main branches: the intracranial segment and the pterygopalatine artery (PtgA), as shown in Fig. 1 A. The upper green arrow highlights the PtgA, while the lower green arrow indicates the intracranial segment of the ICA. 9 The second imaging plane was meticulously planned to comprise the superior sagittal sinus (SSS) and the straight sinus (StS), two principal midline venous structures responsible for deep cerebral venous drainage. The third imaging plane was oriented to capture the transverse sinuses (TS), which serve as major lateral drainage pathways converging toward the sigmoid sinuses. These two latter acquisition planes (Fig. 1 . B ) were specifically designed to enable the quantitative analysis and comparison of the relative contributions of different venous pathways to global cerebral venous drainage. To ensure inter-individual reproducibility, all imaging planes were individually optimized for each animal, with rigorous attention paid to anatomical consistency and precise alignment of the PC-MRI acquisition planes. To support accurate slice planning, a preliminary set of 2D phase-contrast angiographic (2D PCA) (Fig. 2 . B & 3.C ) scans were acquired for each subject. These angiographic reference images facilitated the clear identification of vascular landmarks, enabling consistent and reproducible positioning of the flow-sensitive imaging slices across animals. Image Postprocessing and Segmentation The PC-MRI images were post-processed using in-house software, “Flow” 14 Fig. 3 . The software executes a semi-automatic segmentation algorithm for blood vessels delineation.For this purpose, stationary tissue regions adjacent to the region of interest (ROI) were identified, and their average velocity was considered as the new zero velocity reference. Furthermore, the software includes a de-aliasing correction function for instances where the flow velocity exceeded the velocity encoding (VENC) parameter. Depending on the ECG signal, 12 ± 6 velocity values are reconstructed across the cardiac cycle for each segmented pixel within the ROI. These values are then averaged and multiplied by the ROI’s area to calculate the total dynamic flow rate across the ROI over the entire cardiac cycle. Thus, the software can extract the cerebral arterial and venous dynamic flow rate in the selected vessel. The location and size of the ROI are assumed to be constant throughout the cycle. Data Normalization The number of frames (images) acquired per cardiac cycle is non uniform . This variability arises primarily from fluctuations in the animal's ECG signal and the use of prospective cardiac gating , which results in inconsistent temporal sampling across acquisitions. On average, approximately 14 frames per cycle were recorded, with an observed variation of ± 4 frames . To ensure temporal standardization and enable consistent quantitative comparison of flow waveforms , a cubic interpolation strategy was implemented using Python . Specifically, each time series extracted from the raw datasets was interpolated to produce a uniform length of 18 equally spaced time points , providing a normalized representation of the cardiac cycle across all animals and vascular structures. Flow Quantification, Statistical Comparison, and Coupling Analysis The common carotid artery (CCA) flow dynamic curve was calculated by summing the flows in the internal carotid (ICA), external carotid (ECA), and pterygopalatine (PTgal) arteries. Total arterial inflow was defined as the sum of all major feeding arteries visible in imaging plane 1, including the ICAs, basilar artery (BA), ECAs, and PTgals. Venous outflow was calculated as the sum of the flow rates from the extracranial venous drainage structures visible in the imaging plane 1, namely the posterior facial veins (PFVs), which drain into the external jugular veins, and the internal petrosal veins IPets (two visible structures in imaging plane 1). A two-sample t-test was performed to compare the measured flow rates between each vascular structure. Flow curve analysis was then conducted for the feeding arteries (ECA, ICA, BA, PTgal) and for the venous outflow structures (Ipets, PFV, TRS). Arteriovenous net flow was derived by subtracting total venous outflow from total arterial inflow, allowing quantification of the difference between cerebral inflow and outflow. Finally, the hemodynamic coupling between total arterial inflow and total venous outflow was assessed across all subjects using Pearson’s correlation and linear regression. This analysis, performed on aggregated flow values, aimed to evaluate the strength of the relationship between global cerebral inflow and outflow, thereby characterizing overall vascular coupling. Declarations Competing Interests: The authors declare no competing interests. Funding: No Funding Author Contribution K.A. performed the acquisitions, data processing, analysis, and manuscript writing. S.F. contributed to the acquisitions. O.B. was responsible for project design, supervision, and manuscript reviewing. Acknowledgement The authors gratefully acknowledge the Bruker Application Team, and in particular Jerome Voiron, for their valuable advice and technical support Data Availability The datasets generated and/or analyzed during the current study, including the flow rate curves used for the analyses, are available from the corresponding author upon reasonable request. References Munakomi, S. & Das, J. M. Intracranial Pressure Monitoring. in StatPearls [Internet] (StatPearls Publishing, (2024). Hua, J. et al. MRI techniques to measure arterial and venous cerebral blood volume. Neuroimage 187 , 17–31 (2019). Pinto, V. L., Tadi, P. & Adeyinka, A. Increased Intracranial Pressure. in StatPearls (StatPearls Publishing, 2025). Peng, S. L. et al. Phase-contrast magnetic resonance imaging for the evaluation of wall shear stress in the common carotid artery of a spontaneously hypertensive rat model at 7T: Location-specific change, regional distribution along the vascular circumference, and reproducibility analysis. Magn. Reson. Imaging . 34 , 624–631 (2016). Moran, P. R. A flow velocity zeugmatographic interlace for NMR imaging in humans. Magn. Reson. Imaging . 1 , 197–203 (1982). Sato, K. et al. Relationship between cerebral arterial inflow and venous outflow during dynamic supine exercise. Physiol. Rep. 5 , e13292 (2017). BR, G., Sharma, P. K., Polaka, Y. & Natarajan, P. S, P. The Role of Phase-Contrast MRI in Diagnosing Cerebrospinal Fluid Flow Abnormalities. Cureus 16, e57114. Ellenbroek, B. & Youn, J. Rodent models in neuroscience research: is it a rat race? Dis. Model. Mech. 9 , 1079–1087 (2016). Oscar, U. Scremin. Cerebral Vascular System. Chiu, S. C., Hsu, S. T., Huang, C. W., Shen, W. C. & Peng, S. L. Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery. J. Vis. Exp. 57304 10.3791/57304 (2018). Gorshkova, O. P. Age-Related Changes in the Indices of Cerebral Blood Flow Velocity in Rats. J. Evol. Biochem. Phys. 58 , 894–900 (2022). Ray, J. W., Sun, X., Cruz-Diaz, N., Pulgar, V. M. & Yamaleyeva, L. M. Sex differences in middle cerebral artery reactivity and hemodynamics independent from changes in systemic arterial stiffness in Sprague–Dawley rats. Physiological Rep. 13 , e70250 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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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01:38:39","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228764,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/cae49d70b61d9202f3368054.png"},{"id":94806229,"identity":"c5b40d9e-e065-44cc-aa17-be0ed33c8973","added_by":"auto","created_at":"2025-10-31 01:38:45","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168228,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/6eb1ef21ed1e5faa3c64fd80.png"},{"id":94806155,"identity":"da7c7e70-e4e2-41f8-ac1f-6f3de1b59603","added_by":"auto","created_at":"2025-10-31 01:38:41","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83343,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/114222dd63691e5b717725bf.png"},{"id":94806153,"identity":"bfa7bc52-f890-447f-9d32-aff9d2f89034","added_by":"auto","created_at":"2025-10-31 01:38:40","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61586,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/dd7863addf53738c5b860dfd.png"},{"id":94806181,"identity":"c3091b34-a867-421e-9a57-1054e1717be4","added_by":"auto","created_at":"2025-10-31 01:38:42","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105563,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/ffe74b38923551c35cc0aee4.png"},{"id":94806151,"identity":"8e974ab8-542d-4efb-9c8e-ddf3636eec86","added_by":"auto","created_at":"2025-10-31 01:38:40","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26025,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/6cceceb6ef11afccf47bfef9.png"},{"id":94806179,"identity":"32edf7e2-4a02-4f6a-a0ba-ce2fed1615a2","added_by":"auto","created_at":"2025-10-31 01:38:42","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104305,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/84c26fad7f4826a2073954b8.png"},{"id":94825977,"identity":"7775c030-c40e-4594-8b09-bdc7ef8c4883","added_by":"auto","created_at":"2025-10-31 06:50:51","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46303,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/b41bf55497d223ed12d05f3d.png"},{"id":94806177,"identity":"f0a8e950-3e94-48ac-8e71-451bfebd3499","added_by":"auto","created_at":"2025-10-31 01:38:42","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35946,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/fba86a065128099b649f15b8.png"},{"id":94806193,"identity":"86dc12c9-6dd7-4569-bafa-dcd168e99708","added_by":"auto","created_at":"2025-10-31 01:38:43","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57842,"visible":true,"origin":"","legend":"","description":"","filename":"1a927da94ee449d2a1521fce5d1bb6081structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/3260e338df0febb77a457478.xml"},{"id":94825216,"identity":"2725d411-73c2-4654-b405-88b5ab6a481b","added_by":"auto","created_at":"2025-10-31 06:49:58","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66241,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/b3310ce42c84a02a9bb9314e.html"},{"id":94825176,"identity":"9256c65f-b302-4c24-827a-e3942d87139b","added_by":"auto","created_at":"2025-10-31 06:49:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":458489,"visible":true,"origin":"","legend":"\u003cp\u003ePlanning of imaging planes. (A) Intracranial imaging plane used to extract the major arteries and veins, shown in panel C(phase and magnitude results from slice 1). Arterial structures include: (1) basilar artery (BA), (2) right internal carotid artery (R-ICA), (3) right external carotid artery (R-ECA), (8) left external carotid artery (L-ECA), (9) left internal carotid artery (L-ICA), and the left pterygopalatine artery (PtGA). Venous structures comprise: (4) right posterior facial vein (R-PFV), (5) right inferior petrosal sinus (R-IPeTS), (6) left inferior petrosal sinus (L-IPeTS), and (7) left posterior facial vein (L-PFV). (B) Imaging plane dedicated to the extraction of major venous sinuses, with the corresponding results displayed in panels D and E (slices 2 and 3). Slice 2 depicts the right and left transverse sinuses (R-TS and L-TS), while slice 3 isolates the superior sagittal sinus (SSS) and the straight sinus (SS).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/85427f04b8e2e25a06ce1e92.png"},{"id":94806147,"identity":"bc94477f-9c5e-4dd5-9bed-5f1ccbb430ab","added_by":"auto","created_at":"2025-10-31 01:38:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":339155,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of imaging planes for cerebral vascular assessment in rats.(A–B) Sagittal views optimized to simultaneously include the internal (ICA) and external (ECA) carotid arteries. (C) Coronal view showing the basilar artery (BA). This imaging orientation enables the simultaneous visualization of the ICA, ECA, and BA, allowing comprehensive assessment of the major arterial inflow pathways to the brain.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/6aad4fe2033d74f445a84a9b.png"},{"id":94806142,"identity":"2a5bfe52-d419-4d1b-8c5d-6ee4555a8d55","added_by":"auto","created_at":"2025-10-31 01:38:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":478415,"visible":true,"origin":"","legend":"\u003cp\u003eExample of segmentation of the external carotid artery using the Flow software. (A) Semi-automatic segmentation of the vessel. (B) Extraction of the region of interest (ROI) and display of associated quantitative parameters, including surface area, volume, and pixel count. (C) Visualization of the mean flow rate curve. (D) Display of velocity curves, including mean, maximum, and minimum velocities.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/b80ac224cfa9087569f71d4e.png"},{"id":94806219,"identity":"8b2ab45c-c45a-41f4-ab2c-9d7b02429333","added_by":"auto","created_at":"2025-10-31 01:38:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195655,"visible":true,"origin":"","legend":"\u003cp\u003eMean arterial blood inflow (ml/min) per vascular structure, with comparison between male and female subjects. Bars represent the average flow rate for each artery, while circles and triangles indicate the maximum observed value per structure. This visualization enables assessment of sex-related differences and flow variability across vascular territories.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/90829c1ab606254071a1a173.png"},{"id":94825851,"identity":"b92d40c4-39a7-47c9-bbff-b0b84f97f92b","added_by":"auto","created_at":"2025-10-31 06:50:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":487779,"visible":true,"origin":"","legend":"\u003cp\u003eCerebral arterial flow dynamics.\u003cbr\u003e\n(A) Internal carotid arteries (left: circles; right: squares): temporal flow profiles (individual curves and mean ± SD) and subject-wise mean flow comparison. (B) External carotid arteries: same representation as in (A). (C) Basilar artery: temporal profiles (individual curves and mean ± SD) and subject-wise mean flow comparison. (D) Common carotid artery: temporal dynamics (individual and mean ± SD) and subject-wise mean flow values.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/dc8f092a8797da0d69f3d173.png"},{"id":94806227,"identity":"4a025a3d-4b7d-4082-b89d-ee5f652c9728","added_by":"auto","created_at":"2025-10-31 01:38:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":228764,"visible":true,"origin":"","legend":"\u003cp\u003eVenous and sinus flow dynamics over the cardiac cycle.\u003cbr\u003e\n(A) Temporal evolution of TRS. Thin curves represent individual trajectories (n = 8), thick curves the population mean ± SD. Circular markers indicate the left side, squares the right side. Bottom panel: individual left–right comparison. (B) Temporal evolution of PFV with the same representation as in (A).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/9db3449a06ae8ab0624bc081.png"},{"id":94806225,"identity":"84ac74f8-c62f-4eb8-bb46-6eb9030c6ba1","added_by":"auto","created_at":"2025-10-31 01:38:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":168228,"visible":true,"origin":"","legend":"\u003cp\u003eNet cerebral blood flow and inflow–outflow correlation. (A) Net flow was computed from arterial inflow (sum of all arteries) and venous outflow (sum of all veins) within acquisition plane 1. (B) Significant correlation between inflow and outflow (R = 0.851, p = 0.007) confirms measurement consistency.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/10e7a7bc327216d3e8b5ee45.png"},{"id":100546588,"identity":"aa3054f2-4042-41f3-b1c9-0e8453c0baea","added_by":"auto","created_at":"2026-01-19 08:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3499580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7730675/v1/ac89f4e0-56a1-4c4e-a651-453dd163d64f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Cerebral blood flow dynamics in rat brain using Phase contrast MRI. Technical challenges and physiological consideration","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cb\u003eIn human, The intracranial compartment is composed of three primary elements: blood, cerebrospinal fluid (CSF), and brain parenchyma. According to the Monro-Kellie doctrine, this compartment is considered incompressible, meaning that the total volume within the cranium remains constant. As such, any volumetric change in one component must be offset by a reciprocal change in one or more of the others to preserve stable intracranial pressure (ICP)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this rich and complex anatomical context, non-invasive assessment of vascular parameters\u0026mdash;such as cerebral blood volume (CBV), cerebral blood flow (CBF), and different arterial and venous flows is crucial for characterizing both normal and pathological cerebral perfusion. Separating this two compartment may enable the detection of early changes in brain physiology.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhile \u003cb\u003espace-occupying lesions\u003c/b\u003e such as tumors, hematomas, or abscesses are recognized causes of ICP elevation, fluctuations in \u003cb\u003eCSF or cerebral blood volumes\u003c/b\u003e may disrupt this equilibrium in case of Clinical conditions including \u003cb\u003ehydrocephalus\u003c/b\u003e, \u003cb\u003evenous sinus thrombosis\u003c/b\u003e, and \u003cb\u003eidiopathic intracranial hypertension (IIH)\u003c/b\u003e that represent a non-mass pathologies that can disrupt intracranial compliance\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA comprehensive understanding of cerebral hemodynamics is essential to elucidate the physiological mechanisms that underlie cerebral perfusion.\u003c/p\u003e\u003cp\u003eDoppler ultrasound (US) and phase-contrast (PC) MRI are the methods of choice for measuring flow-related parameters \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. One of the main advantages of phase-contrast MRI is its high spatial resolution combined with operator-independence, which contributes to a high level of reproducibility in imaging plane placement. Unlike ultrasound, despite its high temporal resolution, it is currently not possible to access deep venous and arterial structures due to the cranium.\u003c/p\u003e\u003cp\u003eThe concept of encoding blood flow velocities using phase was first introduced by Paul R.Moran in the early 1980s. When a pair of bipolar gradients is applied, stationary spins experience no net phase shift, whereas moving spins acquire a phase shift proportional to their velocity. Spins moving at the same speed in opposite directions exhibit equal but opposite phase shifts, allowing velocity to be quantified from phase measurements\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSeveral studies demonstrated the strong relationship between venous and arterial flow notably The study conducted by Sato et al. (2017)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e in human subjects, The authors showed that increased cerebral arterial flow\u0026mdash;measured at the level of the internal carotid artery (ICA) and vertebral artery (VA)\u0026mdash;is accompanied by parallel adjustments in venous drainage via the internal jugular vein (IJV) and vertebral vein (VV), with statistically significant correlations (r\u0026thinsp;=\u0026thinsp;0.73 for ICA\u0026ndash;IJV) These finding suggest the existence of a dynamic coupling between arterial inflow and venous outflow. In addition, recent studies have shown that PC-MRI can detect early alterations in CSF pulsatility, in which stroke volume may reflect changes in intracranial compliance or vascular resistance\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn rats one of the most widely used model organisms in basic neuroscience and brain research\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e the cerebral vascular system is highly complex, characterized by a rich architectural organization that ensures consistent and reliable blood supply. This complexity is manifested in an extensive arterial and venous network, organized around multiple anastomotic circles, notably the Circle of Willis, as well as superficial and deep venous systems. Furthermore, the dural venous sinuses contribute significantly to efficient cerebral venous drainage.\u003c/p\u003e\u003cp\u003eFrom an anatomical perspective, the rat brain is supplied by two distinct vascular systems: the internal carotid system and the vertebrobasilar system. These two systems are interconnected, forming a regulatory network that adjusts blood flow dynamics in response to neuronal demands through feedback and feedforward mechanisms\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNevertheless, to our knowledge, no study in rats has simultaneously examined both arterial and venous structures, even though the arteriovenous dynamics appear to be important for capturing volume changes within the brain. The assessment of flow profiles can provide valuable insights into the impact of pathological conditions on macroscopic blood flow, as well as into the behavior of the arteriovenous system in relation to changes in age, sex, body weight, and strain. \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis study aims to quantify vascular cerebral inflow and outflow dynamic during cardiac cycle, of healthy Spragues dewlays rats using PCMRI.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eArterial and Venous Flow Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the distribution of flow parameters: mean, standard deviation, and maximum values within the arterial, venous, and sinus structures, respectively. These visualizations provide a comprehensive overview of the hemodynamic characteristics observed across all studied regions. In addition, comparisons between male and female subjects were performed to assess potential sex-related differences in blood flow dynamics\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe statistical analysis revealed several significant differences in flow values between male and female rats across venous, sinus, and arterial compartments.\u003c/p\u003e\u003cp\u003eIn the veins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e), significant differences were observed in the right posterior facial vein (R PFV) and left Ipets vein, with flow values reduced in females (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found for the left facial vein (L PFV) and right Ipets vein (R Ipets).\u003c/p\u003e\u003cp\u003eWithin the dural sinuses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eC)\u003c/b\u003e, significant sex-related differences were detected in the superior sagittal sinus (SSS), straight sinus (SS), and left transverse sinus (L TRS), indicating sex-specific vascular organization. No significant difference was observed in the right transverse sinus (R TRS).\u003c/p\u003e\u003cp\u003eRegarding the arterial system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eA)\u003c/b\u003e, females showed significantly lower flow in the left internal carotid artery (L ICA), external carotid arteries (L and R ECA), and both PTGAL arteries. No significant sex effect was found in the right internal carotid artery (R ICA) or the basilar artery (BA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eArterial Structure-Specific Analysis\u003c/h3\u003e\n\u003cp\u003eWe decided to analyze both the ICA, BA and the ECA, considering that these vessels exhibit the greatest hemodynamic pattern.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the blood flow profiles in the external carotid arteries (ECA), internal carotid arteries (ICA), the basilar artery (BA), and the common carotid arteries (CCA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll curves exhibit a characteristic pattern reflecting the influence of the cardiac cycle on cerebral blood flow. This pattern can be decomposed into two main phases:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA systolic phase, characterized by a rapid and progressive rise in flow, typically occupying the first third of the cardiac cycle.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA diastolic phase, marked by a gradual decline followed by relatively stable flow, spanning the remaining two-thirds of the cycle.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe systolic peaks occur predominantly around 50 ms for the ICA and ECA, approximately 45 ms for the CCA, and around 35 ms for the basilar artery. This temporal difference may reflect distinct hemodynamic behaviors between the anterior and posterior cerebral circulation.\u003c/p\u003e\u003cp\u003eSubject F2 shows significantly lower blood flow values in the internal carotid arteries (ICA) compared to the other subjects, with reduced values observed on both the left and right sides.\u003c/p\u003e\n\u003ch3\u003eVenous compartment\u003c/h3\u003e\n\u003cp\u003eThe analysis of transverse sinus (STR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) flow revealed that the mean value on the right side (3 ml/min) was substantially higher than on the left side (1 ml/min). The pulsatility index (PI) was also higher on the right (0.6) compared to the left (0.32). This difference between hemispheres was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0116). For the posterior frontal vein (PFV)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), the mean flow values were 9.1615 ml/min (right) and 6.5308 ml/min (left). No statistically significant difference was observed between hemispheres, with PI values of 0.7 (right) and 0.6 (left).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eNet Flow \u0026 arteriovenous coupling\u003c/h3\u003e\n\u003cp\u003eTo characterize the overall cerebral blood flow dynamics, we analyzed both net arteriovenous flow and the relationship between arterial inflow and venous outflow. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, the arteriovenous net flow curve, obtained by summing arterial inflow and venous outflow, exhibite a clear pulsatile pattern over the cardiac cycle, peaking around 50 ms with values close to +\u0026thinsp;20 ml/min this reflects the instantaneous balance between blood entering and leaving the brain, highlighting the pulsatile dynamics of cerebral circulation over the cardiac cycle. In addition, As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, correlation analysis between mean inflow and mean outflow rates across subjects revealed a strong positive linear relationship (R\u0026thinsp;=\u0026thinsp;0.851, p\u0026thinsp;=\u0026thinsp;0.007). This indicates that higher inflow values are consistently matched by higher outflow, with the regression line confirming a robust proportionality between arterial inflow and venous outflow within the measured range.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates the feasibility of non-invasive mapping of cerebral hemodynamics in rodents using a 7 Tesla MRI system with prospective cardiac triggering. By implementing optimized 2D phase-contrast imaging planes, we successfully quantified both arterial inflow and venous outflow across multiple vascular compartments in Sprague-Dawley rats. A key strength of this approach lies in its capacity to assess multiple vascular segments within a single acquisition plan, enabling coherent and comparative analysis of dynamic blood flow.\u003c/p\u003e\u003cp\u003eOur results revealed a consistent inflow pattern through the internal carotid arteries (ICA), characterized by high pulsatile pattern, which is indicative of strong arterial waveforms and potentially increased vascular stiffness. In contrast, venous outflow showed greater inter-individual variability. This variability may reflect individual differences in venous architecture and collateral pathways. The observed asymmetry between left and right transverse sinuses further reinforces this variability and may point to subject-specific anatomical differences or functional disparities in venous drainage routes.\u003c/p\u003e\u003cp\u003eA simultaneous acquisition strategy for both arterial and venous structures was implemented through tailored slice positioning. This enabled real-time evaluation of arteriovenous dynamics, providing insight into cerebrovascular compliance. Interestingly, a direct and stable correlation was observed between mean arterial inflow and measured venous outflow. This relationship likely reflects the accuracy of PC MRI.\u003c/p\u003e\u003cp\u003eOur cohort exhibited inter-individual variability in mean cerebral blood flow. Subjects with lower arterial inflow demonstrated correspondingly lower cerebral blood flow, whereas higher arterial inflow was consistently associated with higher cerebral blood flow. This relationship was further confirmed by a strong positive linear correlation between mean inflow and outflow rates across subjects (R = 0.851, p = 0.007). The regression analysis highlights a proportional balance between arterial inflow and venous outflow consistent with the physiological principle of flow conservation. Residual variability may reflect inter-individual anatomical differences, measurement noise, or contributions from collateral vascular pathways not directly captured in the primary inflow–outflow assessment.\u003c/p\u003e\u003cp\u003eAs we have shown, inter-individual variability of CBF in rats is significant, similar to what is observed in humans\u003csup\u003e15\u003c/sup\u003e. This variability is expected to affect all MRI sequences that quantify cerebral blood perfusion, such as ASL. In this context, CBF quantified by PC-MRI could serve as a reference\u003c/p\u003e\u003cp\u003eHypertension and neurodegenerative diseases such as Alzheimer’s disease or hydrocephalus appear to be related to cerebral vascular alterations. In such studies, PC-MRI could provide novel biomarkers based on flow dynamics and their pulsatility\u003c/p\u003e\u003cp\u003eThis preliminary work should be extended to larger populations, considering sex, age, and weight, in order to establish reference values for cerebral vascular dynamics. Improvements in the hardware should also enable the quantification of CSF oscillations, thereby complementing investigations of craniospinal neurofluids, which primarily interact with the glymphatic system.\u003c/p\u003e\u003cp\u003eDespite these promising outcomes, several experimental and methodological limitations must be acknowledged. One of the main challenges was maintaining consistent physiological stability across animals. Heart rate and respiration in rodents are highly sensitive to body temperature\u003csup\u003e16\u003c/sup\u003e, and even small fluctuations can influence flow measurements. To mitigate this, the depth of anesthesia was carefully monitored, and temperature control was applied. However, maintaining strictly stable physiological conditions remains technically challenging, particularly over longer acquisitions time due to fluctuations in heart rate\u003c/p\u003e\u003cp\u003eAdditional variability came from differences in animal positioning and body size. The studied groups were not weight-matched, which led to anatomical differences influencing both image geometry and signal-to-noise ratio (SNR). This was further complicated by the use of a surface coil for signal reception, where SNR decreases with depth, potentially impacting flow measurements in deeper structures such as the basilar artery.\u003c/p\u003e\u003cp\u003eSlice positioning also represents a critical limitation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although TOF images were used to guide slice planning, inter-individual anatomical variability and slight differences in positioning—both between and within animals—made precise reproducibility difficult. Even repeated scans of the same animal often resulted in small discrepancies due to cradle placement or minor movements. Nonetheless, overall slice positioning was sufficiently consistent to allow inter-subject comparison.\u003c/p\u003e\u003cp\u003eSeveral technical aspects of acquisition required careful consideration. The number of cardiac frames was determined based on the recorded heart rate, incorporating a 10% margin to accommodate physiological variability. Proper ECG electrode placement, along with conductive gel, was essential to minimize false triggers. Spatial resolution was another important factor: while higher resolution is desirable for small vessels, such as the basilar artery, partial volume effects remain a concern\u003csup\u003e17\u003c/sup\u003e. Unfortunately, it was not feasible to tailor resolution for each structure individually within a single slice, especially when both large and small vessels were included.\u003c/p\u003e\u003cp\u003eFinally, the use of a single velocity encoding (Venc) value per slice imposed another methodological constraint. In slice 1, for instance, both arteries and veins were analyzed using a Venc optimized for high-velocity arterial flow (e.g., 80 cm/s). While this setting is adequate for arteries, it may overestimate or fail to capture slower venous flows. The inability to apply dual-Venc settings within the same slice is a current technical limitation, though future protocols may integrate more adaptive encoding schemes.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\n\n"},{"header":"Methods","content":"\u003ch2\u003eAnimal Model\u003c/h2\u003e\u003cp\u003e All animal procedures were conducted with prior approval from the French Ministry of Higher Education and Research and the local Institutional Animal Care and Use Committee (APAFIS#47673-2024022115164143 v6), in strict accordance with institutional ethical guidelines, the European Directive 2010/63/EU for the protection of animals used for scientific purposes, and the ARRIVE guidelines. A total of eight Sprague-Dawley (SD) rats were used in this study: five females (mean weight: 402 ± 35 g, age: 34 weeks) and three males (mean weight: 580 ± 20 g, age: 15 weekFor each imaging session, animals were anesthetized using 2% isoflurane in 1 L/min oxygen. Induction was carried out in a heated isoflurane chamber, ensuring uniform and stress-free anesthesia initiation. Once anesthetized, animals were positioned on a temperature-regulated water circulation pad to maintain a body temperature of ~ 37°C, and their heads were fixed in place using stereotaxic ear bars to prevent motion during scanning. Physiological monitoring including oxygen saturation, heart rate, respiratory rate, and body temperature were performed using the SA Instruments system\u003csup\u003e\u003cb\u003e13\u003c/b\u003e\u003c/sup\u003e. Isoflurane concentration was adjusted as needed to maintain stable anesthetic depth. In cases of oxygen desaturation, supplemental oxygen was provided to restore physiological parameters and ensure animal welfare.\u003c/p\u003e\u003cp\u003eAnimals were kept alive for potential reuse in a future project (authorization request in preparation); when required, euthanasia was performed by CO₂ chamber in accordance with institutional and EU guidelines.\u003c/p\u003e\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\u003cp\u003eAll animals underwent systematic visual monitoring and regular body weight measurements to ensure their overall health status. High-resolution T2-weighted MRI scans and 3D Time-of-Flight (TOF) angiographic sequences were acquired to confirm the normal cerebral anatomy of the rats. The TOF imaging further enabled detailed assessment of the vascular architecture, providing a non-invasive evaluation of the integrity and distribution of the cerebral vasculature.\u003c/p\u003e\u003ch2\u003eMRI Acquisition Protocol\u003c/h2\u003e\u003cp\u003eMRI acquisitions were performed on a 7 Tesla Bruker BioSpec 70/20 scanner (Bruker, Ettlingen, Germany) equipped with a 630 mT/m gradient insert. A volumetric coil was used for transmission, and signal reception was achieved using a 2×2-element surface array coil. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the MRI acquisition parameters used for cerebrovascular flow assessment, including phase-contrast velocity encoding (Venc) values optimized for arteries, veins, and venous sinuses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMain PC-MRI parameters used in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepetition Time (TR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcho Time (TE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlip Angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20°\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlice Thickness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 mm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eField of View (FOV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 mm × 35 mm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePixel Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1367 mm × 0.1367 mm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcquisition Matrix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e≈ 256 × 256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVenc – Arteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 cm/s\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVenc (Transverse sinuses)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 cm/s\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVenc (SSS \u0026amp; SS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 cm/s\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcquisition Time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e≈ 2 min\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOne of the principal methodological constraints of this study was the necessity to simultaneously capture both arterial inflow and venous outflow within a single imaging plane, thereby ensuring accurate temporal synchronization and volumetric correspondence across the examined vascular structures.\u003c/p\u003e\u003cp\u003eThe primary imaging slice was carefully positioned to encompass key arterial vessels, including the internal carotid arteries (ICA), the basilar artery (BA), and the external carotid arteries (ECA), alongside major venous structures such as the inferior petrosal sinuses (Ipets) and the posterior facial veins (PFV\u003cb\u003e)(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, this plane also included the pterygopalatine arteries (PTgal), which—depending on inter-animal anatomical variability and subject positioning could reliably be visualized within the same slice as the ICA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt is important to emphasize that, anatomically, the ICA bifurcates into two main branches: the intracranial segment and the pterygopalatine artery (PtgA), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The upper green arrow highlights the PtgA, while the lower green arrow indicates the intracranial segment of the ICA.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe second imaging plane was meticulously planned to comprise the superior sagittal sinus (SSS) and the straight sinus (StS), two principal midline venous structures responsible for deep cerebral venous drainage. The third imaging plane was oriented to capture the transverse sinuses (TS), which serve as major lateral drainage pathways converging toward the sigmoid sinuses.\u003c/p\u003e\u003cp\u003eThese two latter acquisition planes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e) were specifically designed to enable the quantitative analysis and comparison of the relative contributions of different venous pathways to global cerebral venous drainage.\u003c/p\u003e\u003cp\u003eTo ensure inter-individual reproducibility, all imaging planes were individually optimized for each animal, with rigorous attention paid to anatomical consistency and precise alignment of the PC-MRI acquisition planes.\u003c/p\u003e\u003cp\u003eTo support accurate slice planning, a preliminary set of 2D phase-contrast angiographic (2D PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e \u0026amp; \u003cb\u003e3.C\u003c/b\u003e) scans were acquired for each subject. These angiographic reference images facilitated the clear identification of vascular landmarks, enabling consistent and reproducible positioning of the flow-sensitive imaging slices across animals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eImage Postprocessing and Segmentation\u003c/h2\u003e\u003cp\u003eThe PC-MRI images were post-processed using in-house software, “Flow”\u003csup\u003e14\u003c/sup\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The software executes a semi-automatic segmentation algorithm for blood vessels delineation.For this purpose, stationary tissue regions adjacent to the region of interest (ROI) were identified, and their average velocity was considered as the new zero velocity reference. Furthermore, the software includes a de-aliasing correction function for instances where the flow velocity exceeded the velocity encoding (VENC) parameter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDepending on the ECG signal, 12 ± 6 velocity values are reconstructed across the cardiac cycle for each segmented pixel within the ROI. These values are then averaged and multiplied by the ROI’s area to calculate the total dynamic flow rate across the ROI over the entire cardiac cycle. Thus, the software can extract the cerebral arterial and venous dynamic flow rate in the selected vessel. The location and size of the ROI are assumed to be constant throughout the cycle.\u003c/p\u003e\u003ch2\u003eData Normalization\u003c/h2\u003e\u003cp\u003e\u003cb\u003eThe number of frames (images) acquired per cardiac cycle is non uniform\u003c/b\u003e. This variability arises primarily from fluctuations in the animal's ECG signal and the use of \u003cb\u003eprospective cardiac gating\u003c/b\u003e, which results in inconsistent temporal sampling across acquisitions. On average, approximately \u003cb\u003e14 frames per cycle\u003c/b\u003e were recorded, with an observed variation of \u003cb\u003e± 4 frames\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo ensure \u003cb\u003etemporal standardization\u003c/b\u003e and enable consistent \u003cb\u003equantitative comparison of flow waveforms\u003c/b\u003e, a \u003cb\u003ecubic interpolation strategy\u003c/b\u003e was implemented using \u003cb\u003ePython\u003c/b\u003e. Specifically, each time series extracted from the raw datasets was interpolated to produce a \u003cb\u003euniform length of 18 equally spaced time points\u003c/b\u003e, providing a \u003cb\u003enormalized representation of the cardiac cycle\u003c/b\u003e across all animals and vascular structures.\u003c/p\u003e\u003ch2\u003eFlow Quantification, Statistical Comparison, and Coupling Analysis\u003c/h2\u003e\u003cp\u003eThe common carotid artery (CCA) flow dynamic curve was calculated by summing the flows in the internal carotid (ICA), external carotid (ECA), and pterygopalatine (PTgal) arteries. Total arterial inflow was defined as the sum of all major feeding arteries visible in imaging plane 1, including the ICAs, basilar artery (BA), ECAs, and PTgals.\u003c/p\u003e\u003cp\u003eVenous outflow was calculated as the sum of the flow rates from the extracranial venous drainage structures visible in the imaging plane 1, namely the posterior facial veins (PFVs), which drain into the external jugular veins, and the internal petrosal veins IPets (two visible structures in imaging plane 1).\u003c/p\u003e\u003cp\u003eA two-sample t-test was performed to compare the measured flow rates between each vascular structure. Flow curve analysis was then conducted for the feeding arteries (ECA, ICA, BA, PTgal) and for the venous outflow structures (Ipets, PFV, TRS).\u003c/p\u003e\u003cp\u003eArteriovenous net flow was derived by subtracting total venous outflow from total arterial inflow, allowing quantification of the difference between cerebral inflow and outflow.\u003c/p\u003e\u003cp\u003eFinally, the hemodynamic coupling between total arterial inflow and total venous outflow was assessed across all subjects using Pearson’s correlation and linear regression. This analysis, performed on aggregated flow values, aimed to evaluate the strength of the relationship between global cerebral inflow and outflow, thereby characterizing overall vascular coupling.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo Funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.A. performed the acquisitions, data processing, analysis, and manuscript writing. S.F. contributed to the acquisitions. O.B. was responsible for project design, supervision, and manuscript reviewing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the Bruker Application Team, and in particular Jerome Voiron, for their valuable advice and technical support\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study, including the flow rate curves used for the analyses, are available from the corresponding author upon reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMunakomi, S. \u0026amp; Das, J. M. Intracranial Pressure Monitoring. in StatPearls [Internet] (StatPearls Publishing, (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHua, J. et al. 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Sex differences in middle cerebral artery reactivity and hemodynamics independent from changes in systemic arterial stiffness in Sprague\u0026ndash;Dawley rats. \u003cem\u003ePhysiological Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, e70250 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bloodflow, Cerebral blood flow, Phase-contrast MRI, Rat model, Hemodynamics, Preclinical imaging","lastPublishedDoi":"10.21203/rs.3.rs-7730675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7730675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"(max 200 words): The cranium is a rigid structure that encloses the brain parenchyma, arterial and venous blood, and cerebrospinal fluid (CSF), all sharing a non-expandable space. This anatomical constraint requires a regulated balance between cerebral blood supply and CSF oscillations to maintain adequate intracranial pressure. In several brain disorders, studies have reported the involvement of these circulations and their interactions with the glymphatic network. The rat, widely used in neuroscience, shares important similarities with the human brain, making it a suitable model for investigating cerebral blood flow dynamics. However, no study has yet specifically examined the arterial and venous contributions to cerebral circulation in detail. In this study, we used phase-contrast magnetic resonance imaging (PC-MRI), the only non-invasive technique that enables a detailed assessment of both arterial inflow and venous outflow within minutes. Eight rats were anesthetized with isoflurane, and three optimized imaging planes were acquired to capture venous and arterial structures. Preliminary results showed a strong correlation between arterial inflow and venous outflow (R = 0.851, p = 0.007), indicating arterio-venous coupling and confirming the consistency of the acquired data. Considerable inter-individual variability was observed on the venous side, which may be related to physiological and the limits of the approach. these findings demonstrate that PC-MRI provides a reliable non-invasive method to assess arterio-venous interactions in the rat brain","manuscriptTitle":"Evaluation of Cerebral blood flow dynamics in rat brain using Phase contrast MRI. Technical challenges and physiological consideration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 01:38:09","doi":"10.21203/rs.3.rs-7730675/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6cd086f-5bdc-4e95-8d90-f81505b356ed","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56899875,"name":"Health sciences/Anatomy"},{"id":56899876,"name":"Physical sciences/Engineering"},{"id":56899877,"name":"Health sciences/Medical research"},{"id":56899878,"name":"Health sciences/Neurology"},{"id":56899879,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-16T11:52:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 01:38:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7730675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7730675","identity":"rs-7730675","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}