Accurate anatomical labeling of intracranial arteries is critical for cerebrovascular diagnosis and hemodynamic analysis, but remains time-consuming and prone to inter-operator variability. While deep learning provides an automated solution, its clinical adoption is limited by the lack of confidence measures. Incorporating uncertainty quantification into automated labeling could enhance interpretability by identifying ambiguous or abnormal regions and support clinical trust, yet this aspect remains underexplored. To address this gap, we introduce an uncertainty-aware deep learning framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n = 35). Three convolutional neural network architectures were evaluated: (1) UNet with residual encoder blocks, (2) CS-Net, an attention-augmented UNet with spatial attention, and (3) nnUNet, a self-configuring framework that adapts architecture and training to dataset characteristics. Confidence was modeled via test-time augmentation (TTA) combined with a novel coordinate-guided strategy to reduce interpolation errors during inference. Generalizability was assessed by evaluating a subset of the public TubeTK ToF-MRA dataset (n = 20). Voxelwise uncertainty maps highlighted anatomical ambiguities, pathological variations, and inconsistencies in manual references, providing intuitive confidence indicators. nnUNet achieved the highest performance (average Dice score 0.93; clDice 0.94; average surface distance 0.35 mm; 95th percentile of Hausdorff distance 4.51 mm), demonstrating robustness in complex vascular regions. On the TubeTK dataset, nnUNet maintained robust generalization (average Dice score 0.87; clDice 0.87; average surface distance 0.42 mm; 95th percentile of Hausdorff distance 5.85 mm). Validation against co-registered 4D flow MRI showed close agreement between flow velocities derived from automated and manual labels, with no significant differences. The proposed framework delivers a scalable, accurate, and uncertainty-aware solution for intracranial artery labeling. By integrating uncertainty quantification, it offers a transparent and clinically trustworthy tool to facilitate cerebrovascular imaging workflows and support subsequent hemodynamic analyses.

Time-resolved three-dimensional phase-contrast MRI (4D Flow MRI) enables non-invasive quantification of blood flow and derivation of hemodynamic parameters. However, its clinical application is limited by low spatial resolution and noise, particularly affecting velocity measurements near vessel walls. Machine learning-based super-resolution has shown promise in addressing these limitations, but challenges remain, not least in recovering near-wall velocities. Generative adversarial networks (GANs) offer a compelling solution, having demonstrated strong capabilities in restoring sharp boundaries in non-medical super-resolution settings. Yet, their application in 4D Flow MRI remains unexplored, with implementation challenged by known issues such as training instability and non-convergence. In this study, we investigate GAN-based super-resolution and denoising in 4D Flow MRI. Training and validation were conducted using patient-specific cerebrovascular in-silico models, converted into synthetic images via an MR-true reconstruction pipeline, with complementary validation on in-vivo acquisitions. A dedicated GAN architecture was implemented and evaluated across three adversarial loss functions: Vanilla, Relativistic, and Wasserstein. Our results demonstrate that the proposed GAN improved near-wall velocity recovery compared to a non-adversarial reference (vector Normalized Root Mean Square Error (vNRMSE): 6.9% vs. 9.6%); however, implementation specifics are critical for stable network training. While Vanilla and Relativistic GANs proved unstable compared to generator-only training (vNRMSE: 8.1% and 7.8% vs. 7.2%), a Wasserstein GAN demonstrated optimal stability and incremental improvement (vNRMSE: 6.9% vs. 7.2%). Moreover, strong in-vivo performance supports clinical translation. Together, these findings highlight the potential of GAN-based super-resolution in enhancing 4D Flow MRI, particularly in challenging cerebrovascular regions, while emphasizing the importance of carefully selecting adversarial training strategies.

Stenosis of major intracranial arteries is a significant cause of stroke, with assessment of trans-stenotic pressure drops being a key marker of functional stenosis severity. Non-invasive methods for quantifying intracranial pressure changes are hence crucial; however, the narrow and tortuous cerebrovascular network poses challenges to traditional assessment methods such as transcranial Doppler. This study investigates the use of novel deep learning-enhanced super-resolution (SR) four-dimensional (4D) flow magnetic resonance imaging (MRI) in combination with a physics-informed virtual work–energy relative pressure technique to quantify pressure drops across stenotic intracranial arteries. Performance was validated in intracranial-mimicking in vitro experiments using pulsatile flow before being transferred into an in vivo cohort of patients with intracranial atherosclerotic disease. Conversion into sub-millimetre SR imaging significantly improved the accuracy of regional relative pressure estimations in the pulsing brain arteries, mitigating biases observed at >1 mm resolution imaging, and agreeing strongly with reference catheter-based invasive measurements across both moderate and severe stenoses. The in vivo analysis also revealed a significant increase in pressure drops when converting into sub-millimetre SR data, underlining the importance of apparent image resolution in a clinical setting. The results highlight the potential of SR 4D flow MRI for non-invasive quantification of cerebrovascular pressure changes in pulsing intracranial arteries across stenotic vessel segments.

Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architectures: (1) a UNet with residual encoder blocks, reflecting commonly used baselines in vascular labeling; (2) CS-Net, an attention-augmented UNet incorporating channel and spatial attention mechanisms for enhanced curvilinear structure recognition; and (3) nnUNet, a self-configuring framework that automates preprocessing, training, and architectural adaptation based on dataset characteristics. Among these, nnUNet achieved the highest labeling performance (average Dice score: 0.922; average surface distance: 0.387 mm), with improved robustness in anatomically complex vessels. To assess predictive confidence, we implemented test-time augmentation (TTA) and introduced a novel coordinate-guided strategy to reduce interpolation errors during augmented inference. The resulting uncertainty maps reliably indicated regions of anatomical ambiguity, pathological variation, or manual labeling inconsistency. We further validated clinical utility by comparing flow velocities derived from automated and manual labels in co-registered 4D Flow MRI datasets, observing close agreement with no statistically significant differences. Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration.

Cephalad fluid shift is strongly implicated in the brain-structural and craniovascular disturbances observed after long-duration spaceflight. It is part of what has been termed the cranial microgravity response (CMR). However, the physiological mechanisms underlying these changes remain unclear. We employed a ~2-h, −10° head-down tilt (HDT) paradigm to investigate the immediate effects of cephalad fluid shift on cranial venous outflow, cephalic venous vasculature, and brain temperature. Twenty-one adults (26.6 ± 2.4 years) underwent four MRI sessions combining 4D-flow measurements, susceptibility-weighed imaging, Time-of-Flight angiography, and MR Spectroscopy. HDT resulted in a significant increase in internal jugular vein cross-sectional area ( p < 0.001), decreased jugular vein flow velocity ( p < 0.001) and normalized mean flowrate ( p < 0.001), a volumetric expansion of the main cervical arteries ( p < 0.001), and a reduction in superior sagittal sinus flow velocity ( p = 0.03). We also found a significant increase in global brain temperature after HDT (0.33°C ± 0.28°C; p < 0.001). These findings provide converging evidence for compromised cranial venous outflow in simulated microgravity, suggesting an upstream impact on the cerebral venous vasculature and thermal homeostasis of the head. Our setup now offers a comprehensive and fast-turnaround framework for the testing of countermeasures against the CMR.

Background: Cardiovascular (CV) risk factors such as hypertension have been linked to accelerated cognitive decline with aging but underlying hemodynamic mechanisms are unclear. Aortic stiffening, quantified by pulse wave velocity (PWV), may contribute to microvascular injury by elevating transmitted pulsatility and triggering vascular adaptations that limit blood flow. Using a novel single-session 4D flow MRI protocol we assessed heart-brain hemodynamics in cognitively asymptomatic adults with and without hypertension. Hypotheses: Age, higher CV disease (CVD) risk, and aortic PWV are associated with amplified intracranial pulsatility and reduced flow/velocity. Methods: Data have been collected in 24 cognitively asymptomatic adults (62.3±7.4 years; 7 males; 8 hypertensive; Telephone-MoCA≥17). CVD risk was determined using the simplified Framingham model, and physical activity was assessed by self-report. CV and intracranial 4D flow research sequences were acquired at 3T (Prisma, Siemens). Preprocessing and segmentation of aorta and Circle of Willis (CoW) were applied. Mean and peak velocity (Vmean, Vmax; m/s), and time-to-peak (TTP; ms) were quantified voxel-wise. Flow (ml/s), pulsatility index (PI), and aortic PWV were quantified as detailed in Fig 1. PWV was inverse transformed (PWV.T; ms/m). Results: Hypertensive participants were older (69.1±7.4 vs 58.9±4.5 years, p<0.01), had higher CoW PI (1.16±0.28 vs 0.99±0.09, p=0.04), and lower CoW Vmax (0.71±0.06 vs 0.79±0.09 m/s, p=0.03) than controls. CVD risk score was associated with higher PI and lower velocities in the CoW (Fig 2). Higher physical activity was associated with higher CoW Vmean, Vmax, and flow (rho=0.47-0.48; p=0.02-0.03) and lower PI (rho=-0.43, p=0.04). Inverse relationships were found for: CoW Vmax with aortic PWV.T (r=-0.46, p=0.03), and CoW PI with TTP in both regions (r aorta =-0.43, p=0.04; r CoW =-0.45; p=0.03). CoW and aortic TTP showed moderate correlation (r=0.41, p=0.04; Fig 3). Conclusion: Preliminary results show correlations between heart and brain hemodynamic measures. Hypertension and CVD risk were related to adverse CoW hemodynamics, while physical activity showed favorable associations. Aortic PWV was related to lower intracranial peak velocity, suggesting a possible downstream effect. Expected association between aortic PWV and CoW PI was not observed. Findings highlight the complexity of heart-brain hemodynamics and support ongoing recruitment for stratified age-matched analysis.

Background and Purpose: The cerebral circulation is highly regulated to maintain brain perfusion, keeping an equilibrium between the brain tissue, cerebrospinal fluid (CSF) and blood of the arterial and venous systems. Cerebral venous drainage abnormalities have been implicated in multiple cerebrovascular diseases. The purpose of this study is to evaluate the relationship between the arterial inflow (AI) and the cerebral venous outflow (CVO) and their correlation with the cardiac outflow in healthy adults and children to understand the role of the emissary veins in normal venous drainage. Materials and Methods: A total of 31 healthy volunteers (24 adults (39.5 ± 16.0) and seven children (3.4 ± 2.2)) underwent intracranial 4D flow with full circle of Willis coverage and 2D PC-MRI at the level of the transverse sinus for measurement of the AI and CVO, respectively. The AI was calculated as the sum of the flow values in the bilateral internal carotid and basilar arteries. The CVO was calculated as the sum of the flow values in the bilateral transverse sinuses. The cardiac outflow was measured via 2D PC-MRI with retrospective ECG gating with images acquired at the proximal ascending aorta (AAo) and descending (DAo) aorta. The ratios of the AI/AAo flow and CVO/AI were calculated to characterize the fraction of cerebral arterial inflow in relation to cardiac outflow and venous blood draining through the transverse sinuses, respectively. Results: The AI and CVO were significantly correlated (r = 0.81, p < 0.001). The CVO constituted approximately 60–70% of the AI. The CVO/AI ratio was significantly lower in children versus adults (p = 0.025). In adults, the negative correlation of the AI with age remained strong (r = −0.81, p < 0.001). However, the CVO was not significantly associated with age. Conclusion: The CVO/AI ratio suggests an important role of the emissary veins, accounting for approximately 30–40% of venous drainage. The lower CVO/AI ratio in children, although partially related to decreased AI with age, suggests a greater role of the emissary veins in childhood, which strongly decreases with age.

Introduction Intracranial 4D flow MRI enables quantitative assessment of hemodynamics in patients with intracranial atherosclerotic disease (ICAD). However, quantitative assessments are still challenging due to the time-consuming vessel segmentation, especially in the presence of stenoses, which can often result in user variability. To improve the reproducibility and robustness as well as to accelerate data analysis, we developed an accurate, fully automated segmentation for stenosed intracranial vessels using deep learning. Methods 154 dual-VENC 4D flow MRI scans (68 ICAD patients with stenosis, 86 healthy controls) were retrospectively selected. Manual segmentations were used as ground truth for training. For automated segmentation, deep learning was performed using a 3D U-Net. 20 randomly selected cases (10 controls, 10 patients) were separated and solely used for testing. Cross-sectional areas and flow parameters were determined in the Circle of Willis (CoW) and the sinuses. Furthermore, the flow conservation error was calculated. For statistical comparisons, Dice scores (DS), Hausdorff distance (HD), average symmetrical surface distance (ASSD), Bland-Altman analyses, and interclass correlations were computed using the manual segmentations from two independent observers as reference. Finally, three stenosis cases were analyzed in more detail by comparing the 4D flow-based segmentations with segmentations from black blood vessel wall imaging (VWI). Results Training of the network took approximately 10 h and the average automated segmentation time was 2.2 ± 1.0 s. No significant differences in segmentation performance relative to two independent observers were observed. For the controls, mean DS was 0.85 ± 0.03 for the CoW and 0.86 ± 0.06 for the sinuses. Mean HD was 7.2 ± 1.5 mm (CoW) and 6.6 ± 3.7 mm (sinuses). Mean ASSD was 0.15 ± 0.04 mm (CoW) and 0.22 ± 0.17 mm (sinuses). For the patients, the mean DS was 0.85 ± 0.04 (CoW) and 0.82 ± 0.07 (sinuses), the HD was 8.4 ± 3.1 mm (CoW) and 5.7 ± 1.9 mm (sinuses) and the mean ASSD was 0.22 ± 0.10 mm (CoW) and 0.22 ± 0.11 mm (sinuses). Small bias and limits of agreement were observed in both cohorts for the flow parameters. The assessment of the cross-sectional lumen areas in stenosed vessels revealed very good agreement (ICC: 0.93) with the VWI segmentation but a consistent overestimation (bias ± LOA: 28.1 ± 13.9%). Discussion Deep learning was successfully applied for fully automated segmentation of stenosed intracranial vasculatures using 4D flow MRI data. The statistical analysis of segmentation and flow metrics demonstrated very good agreement between the CNN and manual segmentation and good performance in stenosed vessels. To further improve the performance and generalization, more ICAD segmentations as well as other intracranial vascular pathologies will be considered in the future.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.

The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field mapping of cerebrovascular hemodynamics. However, estimations are complicated by the narrow and tortuous intracranial vasculature, with accurate image-based quantification directly dependent on sufficient spatial resolution. Further, extended scan times are required for high-resolution acquisitions, and most clinical acquisitions are performed at comparably low resolution (>1 mm) where biases have been observed with regard to the quantification of both flow and relative pressure. The aim of our study was to develop an approach for quantitative intracranial super-resolution 4D Flow MRI, with effective resolution enhancement achieved by a dedicated deep residual network, and with accurate quantification of functional relative pressures achieved by subsequent physics-informed image processing. To achieve this, our two-step approach was trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 15.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s, and cosine similarity: 0.99 ± 0.06 at peak velocity) and flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.56 mL/s at peak flow), and with the coupled physics-informed image analysis allowing for maintained recovery of functional relative pressure throughout the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the quantitative super-resolution approach is applied to an in-vivo volunteer cohort, effectively generating intracranial flow images at <0.5 mm resolution and showing reduced low-resolution bias in relative pressure estimation. Our work thus presents a promising two-step approach to non-invasively quantify cerebrovascular hemodynamics, being applicable to dedicated clinical cohorts in the future.

As atherosclerosis is one of the main causes of death in industrial nations, noninvasive imaging modalities for studying its underlying mechanisms are in great demand. The quantification of hemodynamic parameters such as pulse wave velocity (PWV) assessed by flow sensitive magnetic resonance imaging (MRI) is a promising tool to observe plaque progression in preclinical models. Mostly, a global PWV value is assessed, however, previous studies already pointed to heterogeneous elasticity profiles in the presence of atherosclerotic plaques. Here, we present the measurement of local PWV values in the murine aortic arch assessed by 4D-flow MRI for spatially resolved elasticity measurements.

We present a method to automatically segment 4D flow magnetic resonance imaging (MRI) by identifying net flow effects using the standardized difference of means (SDM) velocity. The SDM velocity quantifies the ratio between the net flow and observed flow pulsatility in each voxel. Vessel segmentation is performed using an F-test, identifying voxels with significantly higher SDM velocity values than background voxels. We compare the SDM segmentation algorithm against pseudo-complex difference (PCD) intensity segmentation of 4D flow measurements in in vitro cerebral aneurysm models and 10 in vitro Circle of Willis (CoW) datasets. We also compared the SDM algorithm to convolutional neural network (CNN) segmentation in 5 thoracic vasculature datasets. The in vitro flow phantom geometry is known, while the ground truth geometries for the CoW and thoracic aortas are derived from high-resolution time-of-flight (TOF) magnetic resonance angiography and manual segmentation, respectively. The SDM algorithm demonstrates greater robustness than PCD and CNN approaches and can be applied to 4D flow data from other vascular territories. The SDM to PCD comparison demonstrated an approximate 48% increase in sensitivity in vitro and 70% increase in the CoW, respectively; the SDM and CNN sensitivities were similar. The vessel surface derived from the SDM method was 46% closer to the in vitro surfaces and 72% closer to the in vitro TOF surfaces than the PCD approach. The SDM and CNN approaches both accurately identify vessel surfaces. The SDM algorithm is a repeatable segmentation method, enabling reliable computation of hemodynamic metrics associated with cardiovascular disease.

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

Changes in regional hemodynamics are indicative of cerebrovascular disease. However, image-based monitoring is complicated by the unique flow and anatomies found in the brain, with accurate estimates requiring beyond state-of-the-art image resolutions. To address this, we combine a deep residual network, 4D Flow MRI, and physics-informed image processing to provide super-resolution flow images and coupled accurate quantification of intracranial relative pressure. The method is trained and validated on patient-specific in-silico data, highlighting how low resolution-biases are mitigated by super-resolution conversion. Data were also effectively generated at <0.5 mm in a representative in-vivo cohort, highlighting the potential of our presented approach.

Mechanisms underlying heart-brain hemodynamic coupling and effects on the brain remain unclear due to challenges of measuring both heart and brain in a single MRI exam. We have developed a comprehensive MRI protocol that incorporates 1) 4D flow MRI of chest, 2) 4D flow MRI of head and 3) structural neuroimaging into one MRI exam to assess cardiovascular and cerebrovascular flow as well as white matter lesions and brain atrophy. This study demonstrates the utility of heart-brain MRI as viable new tool for in vivo evaluation of hemodynamic coupling along the entire heart-brain pathway.

Cardiovascular risk factors have been linked with dementia risk in aging adults. However, mechanisms underlying heart-brain hemodynamic coupling and effects on the brain remain unclear. 4D flow MRI is uniquely poised to systematically evaluate complex hemodynamics along the heart-brain pathway. Owing to advancements yielding shorter scan times, 4D flow MRI can be acquired in approximately 7 minutes (heart) and 10 minutes (brain). This supports practical add-on of 4D flow MRI to neuroimaging scans for comprehensive heart-brain MRI evaluation in under an hour. We aimed to build a heart-brain MRI acquisition and analysis framework to study relationships between hemodynamics and brain structure with age. 17 healthy participants (age = 53.5±16.3[24-76] years) underwent heart-brain MRI at 3T (MAGNETOM Prisma, Siemens, Erlangen, Germany), including a free-breathing whole-chest 4D flow MRI research sequence, intracranial dual-venc 4D flow MRI research sequence, and T1- and T2-weighted structural neuroimaging. Participants were cognitively healthy and screened for history of cardiovascular and cerebrovascular problems that may influence blood flow. 4D flow MRI was used to evaluate blood flow velocities in the aorta and Circle of Willis (Figure 1: left). Mean velocity was determined on a voxel-wise basis over the entire cardiac cycle. Volumetric and cortical thickness analysis was performed with FreeSurfer (v7.3; Figure 1: right). Left and right hippocampal and amygdala volumes were averaged and corrected for intracranial head size. We examined relationships between hemodynamics, volume and thickness measures, and age using Pearson correlations and multiple regression. Mean aortic velocity was associated with age (r = -0.59, p = 0.012), hippocampal volume (r = 0.68, p = 0.003), amygdala volume (r = 0.59, p = 0.012), and inferior temporal thickness (r = 0.49, p = 0.044). Figure 2. Mean velocity in the Circle of Willis (r = -0.49, p = 0.048), amygdala volume (r = -0.66, p = 0.004) and inferior temporal thickness (r = -0.60, p = 0.010) were also associated with age. Using multiple regression, mean aortic velocity was associated with hippocampal volume (p = 0.018) adjusting for age. This study demonstrates capture of comprehensive heart and brain data in a single imaging exam for assessment of age-related relationships in hemodynamics and brain structure. Heart-brain MRI is a promising tool for evaluation of hemodynamic coupling along the entire heart-brain pathway. Grant support: NIH NIA P30AG059988, P30AG072977, K01AG080070; NINDS R21NS122511:

BackgroundIn phase‐contrast (PC) MRI, several dual velocity encoding methods have been proposed to robustly increase velocity‐to‐noise ratio (VNR), including a standard dual‐VENC (SDV), an optimal dual‐VENC (ODV), and bi‐ and triconditional methods.PurposeTo develop a correction method for the ODV approach and to perform a comparison between methods.Study TypeCase–control study.PopulationTwenty‐six volunteers.Field Strength/Sequence1.5 T phase‐contrast MRI with VENCs of 50, 75, and 150 cm/second.AssessmentSince we acquired single‐VENC protocols, we used the background phase from high‐VENC (VENCH) to reconstruct the low‐VENC (VENCL) phase. We implemented and compared the unwrapping methods for different noise levels and also developed a correction of the ODV method.Statistical TestsShapiro–Wilk's normality test, two‐way analysis of variance with homogeneity of variances was performed using Levene's test, and the significance level was adjusted by Tukey's multiple post hoc analysis with Bonferroni (P < 0.05).ResultsStatistical analysis revealed no extreme outliers, normally distributed residuals, and homogeneous variances. We found statistically significant interaction between noise levels and the unwrapping methods. This implies that the number of non‐unwrapped pixels increased with the noise level. We found that for β = VENCL/VENCH = 1/2, unwrapping methods were more robust to noise. The post hoc test showed a significant difference between the ODV corrected and the other methods, offering the best results regarding the number of unwrapped pixels.Data ConclusionsAll methods performed similarly without noise, but the ODV corrected method was more robust to noise at the price of a higher computational time.Level of Evidence4Technical Efficacy Stage1

PurposeDual‐velocity encoded (dual‐venc or DV) 4D flow MRI achieves wide velocity dynamic range and velocity‐to‐noise ratio (VNR), enabling accurate neurovascular flow characterization. To reduce scan time, we present interleaved dual‐venc 4D Flow with independently prescribed, prospectively undersampled spatial resolution of the high‐venc (HV) acquisition: Variable Spatial Resolution Dual Venc (VSRDV).MethodsA prototype VSRDV sequence was developed based on a Cartesian acquisition with eight‐point phase encoding, combining PEAK‐GRAPPA acceleration with zero‐filling in phase and partition directions for HV. The VSRDV approach was optimized by varying z, the zero‐filling fraction of HV relative to low‐venc, between 0%–80% in vitro (realistic neurovascular model with pulsatile flow) and in vivo (n = 10 volunteers). Antialiasing precision, mean and peak velocity quantification accuracy, and test–retest reproducibility were assessed relative to reference images with equal‐resolution HV and low venc (z = 0%).ResultsIn vitro results for all z demonstrated an antialiasing true positive rate at least 95% for = 2 and 5, with no linear relationship to z (p = 0.62 and 0.13, respectively). Bland–Altman analysis for z = 20%, 40%, 60%, or 80% versus z = 0% in vitro and in vivo demonstrated no bias >1% of venc in mean or peak velocity values at any . In vitro mean and peak velocity, and in vivo peak velocity, had limits of agreement within 15%.ConclusionVSRDV allows up to 34.8% scan time reduction compared to PEAK‐GRAPPA accelerated DV 4D Flow MRI, enabling large spatial coverage and dynamic range while maintaining VNR and velocity measurement accuracy.