Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.

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.

lGeneralized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.

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.

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update.

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.

Automatic 4D Flow MRI Segmentation Using the Standardized Difference of Means Velocity.

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.

Comparison of Improved Unidirectional Dual Velocity-Encoding MRI Methods.

BACKGROUND: In 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. PURPOSE: To develop a correction method for the ODV approach and to perform a comparison between methods. STUDY TYPE: Case-control study. POPULATION: Twenty-six volunteers. FIELD STRENGTH/SEQUENCE: 1.5 T phase-contrast MRI with VENCs of 50, 75, and 150 cm/second. ASSESSMENT: Since 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 TESTS: Shapiro-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). RESULTS: Statistical 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 ß = 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 CONCLUSIONS: All 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 EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

4D flow MRI in abdominal vessels: prospective comparison of k-t accelerated free breathing acquisition to standard respiratory navigator gated acquisition.

Volumetric phase-contrast magnetic resonance imaging with three-dimensional velocity encoding (4D flow MRI) has shown utility as a non-invasive tool to examine altered blood flow in chronic liver disease. Novel 4D flow MRI pulse sequences with spatio-temporal acceleration can mitigate the long acquisition times of standard 4D flow MRI, which are an impediment to clinical adoption. The purpose of our study was to demonstrate feasibility of a free-breathing, spatio-temporal (k-t) accelerated 4D flow MRI acquisition for flow quantification in abdominal vessels and to compare its image quality, flow quantification and inter-observer reproducibility with a standard respiratory navigator-gated 4D flow MRI acquisition. Ten prospectively enrolled patients (M/F: 7/3, mean age = 58y) with suspected portal hypertension underwent both 4D flow MRI acquisitions. The k-t accelerated acquisition was approximately three times faster (3:11 min ± 0:12 min/9:17 min ± 1:41 min, p < 0.001) than the standard respiratory-triggered acquisition. Vessel identification agreement was substantial between acquisitions and observers. Average flow had substantial inter-sequence agreement in the portal vein and aorta (CV < 15%) and poorer agreement in hepatic and splenic arteries (CV = 11-38%). The k-t accelerated acquisition recorded reduced velocities in small arteries and reduced splenic vein flow. Respiratory gating combined with increased acceleration and spatial resolution are needed to improve flow measurements in these vessels.

Accelerated dual-venc 4D flow MRI with variable high-venc spatial resolution for neurovascular applications.

PURPOSE: Dual-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). METHODS: A 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%). RESULTS: In vitro results for all z demonstrated an antialiasing true positive rate at least 95% for RPEAK-GRAPPA$$ {R}_{\mathrm{PEAK}-\mathrm{GRAPPA}} $$  = 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 RZF$$ {R}_{\mathrm{ZF}} $$ . In vitro mean and peak velocity, and in vivo peak velocity, had limits of agreement within 15%. CONCLUSION: VSRDV 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.

Intracranial Blood Flow Quantification by Accelerated Dual-venc 4D Flow MRI: Comparison With Transcranial Doppler Ultrasound.

BACKGROUND: Dual-venc 4D flow MRI, recently introduced for the assessment of intracranial hemodynamics, may provide a promising complementary approach to well-established tools such as transcranial Doppler ultrasound (TCD) and overcome some of their disadvantages. However, data comparing intracranial flow measures from dual-venc 4D flow MRI and TCD are lacking. PURPOSE: To compare cerebral blood flow velocity measures derived from dual-venc 4D flow MRI and TCD. STUDY TYPE: Prospective cohort. SUBJECTS: A total of 25 healthy participants (56 ± 4 years old, 44% female). FIELD STRENGTH/SEQUENCE: A 3 T/dual-venc 4D flow MRI using a time-resolved three-dimensional phase-contrast sequence with three-dimensional velocity encoding. ASSESSMENT: Peak velocity measurements in bilateral middle cerebral arteries (MCA) were quantified from dual-venc 4D flow MRI and TCD. The MRI data were quantified by two independent observers (S.M and Y.M.) and TCD was performed by a trained technician (A.L.M.). We assessed the agreement between 4D flow MRI and TCD measures, and the interobserver agreement of 4D flow MRI measurements. STATISTICAL TESTS: Peak velocity from MRI and TCD was compared using Bland-Altman analysis and coefficient of variance. Intraclass correlation coefficient (ICC) was used to assess MRI interobserver agreement. A P value < 0.05 was considered statistically significant. RESULTS: There was excellent interobserver agreement in dual-venc 4D flow MRI-based measurements of peak velocity in bilateral MCA (ICC = 0.97 and 0.96 for the left and right MCA, respectively). Dual-venc 4D flow MRI significantly underestimated peak velocity in the left and right MCA compared to TCD (bias = 0.13 [0.59, -0.33] m/sec and 0.15 [0.47, -0.17] m/sec, respectively). The coefficient of variance between dual-venc 4D flow MRI and TCD measurements was 26% for the left MCA and 22% for the right MCA. DATA CONCLUSION: There was excellent interobserver agreement for the assessment of MCA peak velocity using dual-venc 4D flow MRI, and ≤20% under-estimation compared with TCD. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Why do humans undergo an adiposity rebound? Exploring links with the energetic costs of brain development in childhood using MRI-based 4D measures of total cerebral blood flow.

BACKGROUND: Individuals typically show a childhood nadir in adiposity termed the adiposity rebound (AR). The AR serves as an early predictor of obesity risk, with early rebounders often at increased risk; however, it is unclear why this phenomenon occurs, which could impede understandings of weight gain trajectories. The brain's energy requirements account for a lifetime peak of 66% of the body's resting metabolic expenditure during childhood, around the age of the AR, and relates inversely to weight gain, pointing to a potential energy trade-off between brain development and adiposity. However, no study has compared developmental trajectories of brain metabolism and adiposity in the same individuals, which would allow a preliminary test of a brain-AR link. METHODS: We used cubic splines and generalized additive models to compare age trajectories of previously collected MRI-based 4D flow measures of total cerebral blood flow (TCBF), a proxy for cerebral energy use, to the body mass index (BMI) in a cross-sectional sample of 82 healthy individuals (0-60 years). We restricted our AR analysis to pre-pubertal individuals (0-12 years, n = 42), predicting that peak TCBF would occur slightly after the BMI nadir, consistent with evidence that lowest BMI typically precedes the nadir in adiposity. RESULTS: TCBF and the BMI showed inverse trajectories throughout childhood, while the estimated age at peak TCBF (5.6 years) was close but slightly later than the estimated age of the BMI nadir (4.9 years). CONCLUSIONS: The timing of peak TCBF in this sample points to a likely concordance between peak brain energetics and the nadir in adiposity. Inverse age trajectories between TCBF and BMI support the hypothesis that brain metabolism is a potentially important influence on early life adiposity. These findings also suggest that experiences influencing the pattern of childhood brain energy use could be important predictors of body composition trajectories.

Modeling Bias Error in 4D Flow MRI Velocity Measurements.

We present a model to estimate the bias error of 4D flow magnetic resonance imaging (MRI) velocity measurements. The local instantaneous bias error is defined as the difference between the expectation of the voxel's measured velocity and actual velocity at the voxel center. The model accounts for bias error introduced by the intra-voxel velocity distribution and partial volume (PV) effects. We assess the intra-voxel velocity distribution using a 3D Taylor Series expansion. PV effects and numerical errors are considered using a Richardson extrapolation. The model is applied to synthetic Womersley flow and in vitro and in vivo 4D flow MRI measurements in a cerebral aneurysm. The bias error model is valid for measurements with at least 3.75 voxels across the vessel diameter and signal-to-noise ratio greater than 5. All test cases exceeded this diameter to voxel size ratio with diameters, isotropic voxel sizes, and velocity ranging from 3-15mm, 0.5-1mm, and 0-60cm/s, respectively. The model accurately estimates the bias error in voxels not affected by PV effects. In PV voxels, the bias error is an order of magnitude higher, and the accuracy of the bias error estimation in PV voxels ranges from 67.3% to 108% relative to the actual bias error. The bias error estimated for in vivo measurements increased two-fold at systole compared to diastole in partial volume and non-partial volume voxels, suggesting the bias error varies over the cardiac cycle. This bias error model quantifies 4D flow MRI measurement accuracy and can help plan 4D flow MRI scans.

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI.

PURPOSE: Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image-based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image-based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full-field virtual work-energy relative pressure ( ν WERP) method. METHODS: Patient-specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and ν WERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers ( n=8 ), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). RESULTS: The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm ν WERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of -2.18 ± 1.91 and -2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using ν WERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). CONCLUSION: Image-based full-field methods such as ν WERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

Development of a rotation phantom for phase contrast MRI sequence validation and quality control.

PURPOSE: To develop a reliable, consistent, and reproducible reference phantom for error quantification of phase-contrast MRI so it can be used for validation and quality control. METHODS: An air-driven rotation phantom consisting of a steadily rotating cylinder surrounded by a static ring both filled with agarose gel was developed. Rotational speed was measured and controlled in real time using an optical counter and a closed-loop controller. Consistency of the phantom was assessed by recording variations in rotational speed. The phantom was imaged with 2D phase-contrast MRI, and the velocity at each point was compared with analytically predicted velocity. Additionally, to examine reproducibility, the phantom was run with the same rotational speed on 2 different days and imaged using the same phase-contrast MRI protocol. RESULTS: The rotation phantom provided consistent rotational speed with 2 revolutions per minute SD from the set value for 20 min. Comparison between predicted and measured velocities demonstrated excellent agreement (intraclass correlation coefficient of 0.99). The RMS error in velocity components were less than 1% of maximum value. The scan-rescan experiment showed that the phantom can reproduce the same velocity distributions (intraclass correlation coefficient of 0.99) using the same rotational speed and MRI settings. CONCLUSION: The developed rotation phantom provided well-defined and reproducible linear velocity distributions, which can be used for systematic and quantitative error analysis of phase-contrast MRI for a range of known velocities.

Bilateral Multiband 4D Flow MRI of the Carotid Arteries at 7T.

PURPOSE: Simultaneous multislab (SMSb) 4D flow MRI was developed and implemented at 7T for accelerated acquisition of the 3D blood velocity vector field in both carotid bifurcations. METHODS: SMSb was applied to 4D flow to acquire blood velocities in both carotid bifurcations in sagittal orientation using a local transmit/receive coil at 7T. B1+ transmit efficiency was optimized by B1+ shimming. SMSb 4D flow was obtained in 8 healthy subjects in single-band (SB) and multiband (MB) fashion. Additionally, MB data were retrospectively undersampled to simulate GRAPPA R = 2 (MB2_GRAPPA2), and both SB datasets were added to form an artificial MB dataset (SumSB). The band separation performance was quantified by signal leakage. Peak velocity and total flow values were calculated and compared to SB via intraclass correlation analysis (ICC). RESULTS: Clean slab separation was achieved yielding a mean signal leakage of 13% above the mean SB noise level. Mean total flow for MB2, SumSB, and MB_GRAPPA2 deviated less than 9% from the SB values. Peak velocities averaged over all vessels and subjects were 0.48 ± 0.11 m/s for SB, 0.47 ± 0.12 m/s for SumSB, 0.50 ± 0.13 m/s for MB2, and 0.53 ± 0.13 m/s for MB2_GRAPPA2. ICC revealed excellent absolute agreement and consistency of total flow for all methods compared to SB2. Peak velocity showed good to excellent agreement and consistency for SumSB and MB2 and MB2_GRAPPA2 method showed poor to excellent agreement and good to excellent consistency. CONCLUSION: Simultaneous multislab 4D Flow MRI allows accurate quantification of total flow and peak velocity while reducing scan times.

Efficient triple-VENC phase-contrast MRI for improved velocity dynamic range.

PURPOSE: To evaluate the utility of an efficient triple velocity-encoding (VENC) 4D flow MRI implementation to improve velocity unwrapping of 4D flow MRI data with the same scan time as an interleaved dual-VENC acquisition. METHODS: A balanced 7-point acquisition was used to derive 3 sets of 4D flow images corresponding to 3 different VENCs. These 3 datasets were then used to unwrap the aliased lowest VENC into a minimally aliased, triple-VENC dataset. Triple-VENC MRI was evaluated and compared with dual-VENC MRI over 3 different VENC ranges (50-150, 60-150, and 60-180 cm/s) in vitro in a steadily rotating phantom as well as in a pulsatile flow phantom. In vivo, triple-VENC data of the thoracic aorta were also evaluated in 3 healthy volunteers (2 males, 26-44 years old) with VENC = 50/75/150 cm/s. Two triple-VENC (triconditional and biconditional) and 1 dual-VENC unwrapping algorithms were quantitatively assessed through comparison to a reference, unaliased, single-VENC scan. RESULTS: Triple-VENC 4D flow constant rotation phantom results showed high correlation with the analytical solution (intraclass correlation coefficient = 0.984-0.995, P < .001) and up to a 61% reduction in velocity noise compared with the corresponding single-VENC scans (VENC = 150, 180 cm/s). Pulsatile flow phantom experiments demonstrated good agreement between triple-VENC and single-VENC acquisitions (peak flow < 0.8% difference; peak velocity < 11.7% difference). Triconditional triple-VENC unwrapping consistently outperformed dual-VENC unwrapping, correctly unwrapping more than 83% and 46%-66% more voxels in vitro and in vivo, respectively. CONCLUSION: Triple-VENC 4D flow MRI adds no additional scan time to dual-VENC MRI and has the potential for improved unwrapping to extend the velocity dynamic range beyond dual-VENC methods.

4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares.

This work introduces a 4D flow magnetic resonance imaging (MRI) pressure reconstruction method which employs weighted least-squares (WLS) for pressure integration. Pressure gradients are calculated from the velocity fields, and velocity errors are estimated from the velocity divergence for incompressible flow. Pressure gradient errors are estimated by propagating the velocity errors through Navier-Stokes momentum equation. A weight matrix is generated based on the pressure gradient errors, then employed for pressure reconstruction. The pressure reconstruction method was demonstrated and analyzed using synthetic velocity fields as well as Poiseuille flow measured using in vitro 4D flow MRI. Performance of the proposed WLS method was compared to the method of solving the pressure Poisson equation which has been the primary method used in the previous studies. Error analysis indicated that the proposed method is more robust to velocity measurement errors. Improvement on pressure results was found to be more significant for the cases with spatially-varying velocity error level, with reductions in error ranging from 50% to over 200%. Finally, the method was applied to flow in patient-specific cerebral aneurysms. Validation was performed with in vitro flow data collected using Particle Tracking Velocimetry (PTV) and in vivo flow measurement obtained using 4D flow MRI. Pressure calculated by WLS, as opposed to the Poisson equation, was more consistent with the flow structures and showed better agreement between the in vivo and in vitro data. These results suggest the utility of WLS method to obtain reliable pressure field from clinical flow measurement data.

Multi-modality cerebral aneurysm haemodynamic analysis: in vivo 4D flow MRI, in vitro volumetric particle velocimetry and in silico computational fluid dynamics.

Typical approaches to patient-specific haemodynamic studies of cerebral aneurysms use image-based computational fluid dynamics (CFD) and seek to statistically correlate parameters such as wall shear stress (WSS) and oscillatory shear index (OSI) to risk of growth and rupture. However, such studies have reported contradictory results, emphasizing the need for in-depth multi-modality haemodynamic metric evaluation. In this work, we used in vivo 4D flow MRI data to inform in vitro particle velocimetry and CFD modalities in two patient-specific cerebral aneurysm models (basilar tip and internal carotid artery). Pulsatile volumetric particle velocimetry experiments were conducted, and the particle images were processed using Shake-the-Box, a particle tracking method. Distributions of normalized WSS and relative residence time were shown to be highly yet inconsistently affected by minor flow field and spatial resolution variations across modalities, and specific relationships among these should be explored in future work. Conversely, OSI, a non-dimensional parameter, was shown to be more robust to the varying assumptions, limitations and spatial resolutions of each subject and modality. These results suggest a need for further multi-modality analysis as well as development of non-dimensional haemodynamic parameters and correlation of such metrics to aneurysm risk of growth and rupture.

Standardized Evaluation of Cerebral Arteriovenous Malformations Using Flow Distribution Network Graphs and Dual-venc 4D Flow MRI.

BACKGROUND: Cerebral arteriovenous malformations (AVMs) are pathological connections between arteries and veins. Dual-venc 4D flow MRI, an extended 4D flow MRI method with improved velocity dynamic range, provides time-resolved 3D cerebral hemodynamics. PURPOSE: To optimize dual-venc 4D flow imaging parameters for AVM; to assess the relationship between spatial resolution, acceleration, and flow quantification accuracy; and to introduce and apply the flow distribution network graph (FDNG) paradigm for storing and analyzing complex neurovascular 4D flow data. STUDY TYPE: Retrospective cohort study. SUBJECTS/PHANTOM: Scans were performed in a specialized flow phantom: 26 healthy subjects (age 41 ± 17 years) and five AVM patients (age 27-68 years). FIELD STRENGTH/SEQUENCE: Dual-venc 4D flow with varying spatial resolution and acceleration factors were performed at 3T field strength. ASSESSMENT: Quantification accuracy was assessed in vitro by direct comparison to measured flow. FDNGs were used to quantify and compare flow, peak velocity (PV), and pulsatility index (PI) between healthy controls with various Circle of Willis (CoW) anatomy and AVM patients. STATISTICAL TESTS: In vitro measurements were compared to ground truth with Student's t-test. In vivo groups were compared with Wilcoxon rank-sum test and Kruskal-Wallis test. RESULTS: Flow was overestimated in all in vitro experiments, by an average 7.1 ± 1.4% for all measurement conditions. Error in flow measurement was significantly correlated with number of voxels across the channel (P = 3.11 × 10-28 ) but not with acceleration factor (P = 0.74). For the venous-arterial PV and PI ratios, a significant difference was found between AVM nidal and extranidal circulation (P = 0.008 and 0.05, respectively), and between AVM nidal and healthy control circulation (P = 0.005 and 0.003, respectively). DATA CONCLUSION: Dual-venc 4D flow MRI and standardized FDNG analysis might be feasible in clinical applications. Venous-arterial ratios of PV and PI are proposed as network-based biomarkers characterizing AVM nidal hemodynamics. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1718-1730.

Semi-automated analysis of 4D flow MRI to assess the hemodynamic impact of intracranial atherosclerotic disease.

PURPOSE: This study evaluated the feasibility of using 4D flow MRI and a semi-automated analysis tool to assess the hemodynamic impact of intracranial atherosclerotic disease (ICAD). The ICAD impact was investigated by evaluating pressure drop (PD) at the atherosclerotic stenosis and changes in cerebral blood flow distribution in patients compared to healthy controls. METHODS: Dual-venc 4D flow MRI was acquired in 25 healthy volunteers and 16 ICAD patients (ICA, N = 3; MCA, N = 13) with mild (<50%), moderate (50-69%), or severe (>70%) intracranial stenosis. A semi-automated analysis tool was developed to quantify velocity and flow from 4D flow MRI and to evaluate cerebral blood flow redistribution. PD at stenosis was estimated using the Bernoulli equation. The PD calculation was examined by an in vitro phantom study against flow simulations. RESULTS: Flow analysis in controls indicated symmetry in blood flow rate (FR) and peak velocity (PV) between the brain hemispheres. For patients, PV in the affected hemisphere was significantly (65%) higher than the normal side (P = 0.002). However, FR to both hemispheres of the brain was the same. The PD depicted significant correlation with PV asymmetry in patients (ρ = 0.67 and P = 0.02), and it was significantly higher for severe compared to moderate stenosis (3.73 vs. 2.30 mm Hg, P = 0.02). CONCLUSION: 4D flow MRI quantification enables assessment of the hemodynamic impact of ICAD. The significant difference of the PD between patients with severe and moderate stenosis and its correlation with PV asymmetry suggest that PD may be a pertinent hemodynamic biomarker to evaluate ICAD.

Aortic 4D flow MRI in 2 minutes using compressed sensing, respiratory controlled adaptive k-space reordering, and inline reconstruction.

PURPOSE: To evaluate the accuracy and feasibility of a free-breathing 4D flow technique using compressed sensing (CS), where 4D flow imaging of the thoracic aorta is performed in 2 min with inline image reconstruction on the MRI scanner in less than 5 min. METHODS: The 10 in vitro 4D flow MRI scans were performed with different acceleration rates on a pulsatile flow phantom (9 CS acceleration factors [R = 5.4-14.1], 1 generalized autocalibrating partially parallel acquisition [GRAPPA] R = 2). Based on in vitro results, CS-accelerated 4D flow of the thoracic aorta was acquired in 20 healthy volunteers (38.3 ± 15.2 years old) and 11 patients with aortic disease (61.3 ± 15.1 years) with R = 7.7. A conventional 4D flow scan was acquired with matched spatial coverage and temporal resolution. RESULTS: CS depicted similar hemodynamics to conventional 4D flow in vitro, and in vivo, with >70% reduction in scan time (volunteers: 1:52 ± 0:25 versus 7:25 ± 2:35 min). Net flow values were within 3.5% in healthy volunteers, and voxel-by-voxel comparison demonstrated good agreement. CS significantly underestimated peak velocities (vmax ) and peak flow (Qmax ) in both volunteers and patients (volunteers: vmax , -16.2% to -9.4%, Qmax : -11.6% to -2.9%, patients: vmax , -11.2% to -4.0%; Qmax , -10.2% to -5.8%). CONCLUSION: Aortic 4D flow with CS is feasible in a two minute scan with less than 5 min for inline reconstruction. While net flow agreement was excellent, CS with R = 7.7 produced underestimation of Qmax and vmax ; however, these were generally within 13% of conventional 4D flow-derived values. This approach allows 4D flow to be feasible in clinical practice for comprehensive assessment of hemodynamics.

4-D flow MRI aortic 3-D hemodynamics and wall shear stress remain stable over short-term follow-up in pediatric and young adult patients with bicuspid aortic valve.

BACKGROUND: Children with bicuspid aortic valve (BAV) are at risk for serious complications including aortic valve stenosis and aortic rupture. Most studies investigating biomarkers predictive of BAV complications are focused on adults. OBJECTIVE: To investigate whether hemodynamic parameters change over time in children and young adults with BAV by comparing baseline and follow-up four-dimensional (4-D) flow MRI examinations. MATERIALS AND METHODS: We retrospectively included 19 children and young adults with BAV who had serial 4-D flow MRI exams (mean difference in scan dates 1.8±1.0 [range, 0.6-3.4 years]). We compared aortic peak blood flow velocity, three-dimensional (3-D) wall shear stress, aortic root and ascending aortic (AAo) z-scores between baseline and follow-up exams. We generated systolic streamlines for all patients and visually compared their baseline and follow-up exams. RESULTS: The only significant difference between baseline and follow-up exams occurred in AAo z-scores (3.12±2.62 vs. 3.59±2.76, P<0.05) indicating growth of the AAo out of proportion to somatic growth. There were no significant changes in either peak velocity or 3-D wall shear stress between baseline and follow-up exams. Ascending aortic peak velocity at baseline correlated with annual change in AAo z-score (r=0.58, P=0.009). Visual assessment revealed abnormal blood flow patterns, which were unique to each patient and remained stable between baseline and follow-up exams. CONCLUSION: In our pediatric and young adult BAV cohort, hemodynamic markers and systolic blood flow patterns remained stable over short-term follow-up despite significant AAo growth, suggesting minimal acute disease progression. Baseline AAo peak velocity was a predictor of AAo dilation and might help in determining pediatric patients with BAV who are at risk of increased AAo growth.