Toward accurate and fast velocity quantification with 3D ultrashort TE phase-contrast imaging.

PURPOSE: Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE (PC-UTE) sequence to reduce displacement artifacts. Furthermore, it investigates the displacement of a standard Cartesian flow sequence by utilizing a displacement-free synchronized-single-point-imaging MR sequence (SYNC-SPI) that requires clinically prohibitively long acquisition times. METHODS: 3D flow data was acquired at 3T at three different constant flow rates and varying spatial resolutions in a stenotic aorta phantom using the proposed PC-UTE, a Cartesian flow sequence, and a SYNC-SPI sequence as reference. Expected displacement artifacts were calculated from gradient timing waveforms and compared to displacement values measured in the in vitro flow experiments. RESULTS: The PC-UTE sequence reduces displacement and intravoxel dephasing, leading to decreased geometric distortions and signal cancellations in magnitude images, and more spatially accurate velocity quantification compared to the Cartesian flow acquisitions; errors increase with velocity and higher spatial resolution. CONCLUSION: PC-UTE MRI can measure velocity vector fields with greater accuracy than Cartesian acquisitions (although pulsatile fields were not studied) and shorter scan times than SYNC-SPI. As such, this approach is superior to traditional Cartesian 3D and 4D flow MRI when spatial misrepresentations cannot be tolerated, for example, when computational fluid dynamics simulations are compared to or combined with in vitro or in vivo measurements, or regional parameters such as wall shear stress are of interest.

Evaluating a Phase-Specific Approach to Aortic Flow: A 4D Flow MRI Study.

BACKGROUND: Aortic flow parameters can be quantified using 4D flow MRI. However, data are sparse on how different methods of analysis influence these parameters and how these parameters evolve during systole. PURPOSE: To assess multiphase segmentations and multiphase quantification of flow-related parameters in aortic 4D flow MRI. STUDY TYPE: Prospective. POPULATION: 40 healthy volunteers (50% male, 28.9 ± 5.0 years) and 10 patients with thoracic aortic aneurysm (80% male, 54 ± 8 years). FIELD STRENGTH/SEQUENCE: 4D flow MRI with a velocity encoded turbo field echo sequence at 3 T. ASSESSMENT: Phase-specific segmentations were obtained for the aortic root and the ascending aorta. The whole aorta was segmented in peak systole. In all aortic segments, time to peak (TTP; for flow velocity, vorticity, helicity, kinetic energy, and viscous energy loss) and peak and time-averaged values (for velocity and vorticity) were calculated. STATISTICAL TESTS: Static vs. phase-specific models were assessed using Bland-Altman plots. Other analyses were performed using phase-specific segmentations for aortic root and ascending aorta. TTP for all parameters was compared to TTP of flow rate using paired t-tests. Time-averaged and peak values were assessed using Pearson correlation coefficient. P < 0.05 was considered statistically significant. RESULTS: In the combined group, velocity in static vs. phase-specific segmentations differed by 0.8 cm/sec for the aortic root, and 0.1 cm/sec (P = 0.214) for the ascending aorta. Vorticity differed by 167 sec-1 mL-1 (P = 0.468) for the aortic root, and by 59 sec-1 mL-1 (P = 0.481) for the ascending aorta. Vorticity, helicity, and energy loss in the ascending aorta, aortic arch, and descending aorta peaked significantly later than flow rate. Time-averaged velocity and vorticity values correlated significantly in all segments. DATA CONCLUSION: Static 4D flow MRI segmentation yields comparable results as multiphase segmentation for flow-related parameters, eliminating the need for time-consuming multiple segmentations. However, multiphase quantification is necessary for assessing peak values of aortic flow-related parameters. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3.

Evaluation of 4D Flow MRI-Derived Relative Residence Time as a Marker for Cirrhosis Associated Portal Vein Thrombosis.

BACKGROUND: Portal vein thrombosis (PVT) is thought to arise from stagnant blood flow, yet conclusive evidence is lacking. Relative residence time (RRT) assessed using 4D Flow MRI may offer insight into portal flow stagnation. PURPOSE: To explore the relationship between RRT values and the presence of PVT in cirrhotic participants. STUDY TYPE: Prospective. POPULATION: Forty-eight participants with liver cirrhosis (27 males, median age 67 years [IQR: 57-73]) and 20 healthy control participants (12 males, median age 45 years [IQR: 40-54]). FIELD STRENGTH/SEQUENCE: 3 T/4D Flow MRI. ASSESSMENT: Laboratory (liver and kidney function test results and platelet count) and clinical data (presence of tumors and other imaging findings), and portal hemodynamics derived from 4D Flow MRI (spatiotemporally averaged RRT [RRT-mean], flow velocity, and flow rate) were analyzed. STATISTICAL TESTS: We used multivariable logistic regression, adjusted by selected covariates through the Lasso method, to explore whether RRT-mean is an independent risk factor for PVT. The area under the ROC curve (AUC) was also calculated to assess the model's discriminative ability. P < 0.05 indicated statistical significance. RESULTS: The liver cirrhosis group consisted of 16 participants with PVT and 32 without PVT. Higher RRT-mean values (odds ratio [OR] 11.4 [95% CI: 2.19, 118]) and lower platelet count (OR 0.98 per 1000 µL [95% CI: 0.96, 0.99]) were independent risk factors for PVT. The incorporation of RRT-mean (AUC, 0.77) alongside platelet count (AUC, 0.75) resulted in an AUC of 0.84. When including healthy control participants, RRT-mean had an adjusted OR of 12.4 and the AUC of the combined model (RRT-mean and platelet count) was 0.90. DATA CONCLUSION: Prolonged RRT values and low platelet count were significantly associated with the presence of PVT in cirrhotic participants. RRT values derived from 4D Flow MRI may have potential clinical relevance in the management of PVT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

3D Vortex-Energetics in the Left Pulmonary Artery for Differentiating Pulmonary Arterial Hypertension and Pulmonary Venous Hypertension Groups Using 4D Flow MRI.

BACKGROUND: Pulmonary hypertension (PH) is a life-threatening. Differentiation pulmonary arterial hypertension (PAH) from pulmonary venous hypertension (PVH) is important due to distinct treatment protocols. Invasive right heart catheterization (RHC) remains the reference standard but noninvasive alternatives are needed. PURPOSE/HYPOTHESIS: To evaluate 4D Flow MRI-derived 3D vortex energetics in the left pulmonary artery (LPA) for distinguishing PAH from PVH. STUDY TYPE: Prospective case-control. POPULATION/SUBJECTS: Fourteen PAH patients (11 female) and 18 PVH patients (9 female) diagnosed from RHC, 23 healthy controls (9 female). FIELD STRENGTH/SEQUENCE: 1.5 T; gradient recalled echo 4D flow and balanced steady-state free precession (bSSFP) cardiac cine sequences. ASSESSMENT: LPA 3D vortex cores were identified using the lambda2 method. Peak vortex-contained kinetic energy (vortex-KE) and viscous energy loss (vortex-EL) were computed from 4D flow MRI. Left and right ventricular (LV, RV) stroke volume (LVSV, RVSV) and ejection fraction (LVEF, RVEF) were computed from bSSFP. In PH patients, mean pulmonary artery pressure (mPAP), pulmonary capillary wedge pressure (PCWR) and pulmonary vascular resistance (PVR) were determined from RHC. STATISTICAL TESTS: Mann-Whitney U test for group comparisons, Spearman's rho for correlations, logistic regression for identifying predictors of PAH vs. PVH and develop models, area under the receiver operating characteristic curve (AUC) for model performance. Significance was set at P < 0.05. RESULTS: PAH patients showed significantly lower vortex-KE (37.14 [14.68-78.52] vs. 76.48 [51.07-120.51]) and vortex-EL (9.93 [5.69-25.70] vs. 24.22 [12.20-32.01]) than PVH patients. The combined vortex-KE and LVEF model achieved an AUC of 0.89 for differentiating PAH from PVH. Vortex-EL showed significant negative correlations with mPAP (rho = -0.43), PCWP (rho = 0.37), PVR (rho = -0.64). In the PAH group, PVR was significantly negatively correlated with LPA vortex-KE (rho = -0.73) and vortex-EL (rho = -0.71), and vortex-KE significantly correlated with RVEF (rho = 0.69), RVSV, (rho = 0.70). In the PVH group, vortex-KE (rho = 0.52), vortex-EL significantly correlated with RVSV (rho = 0.58). DATA CONCLUSION: These preliminary findings suggest that 4D flow MRI-derived LPA vortex energetics have potential to noninvasively differentiate PAH from PVH and correlate with invasive hemodynamic parameters. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Cerebral Sinus Hemodynamics in Adults Revealed by 4D Flow MRI.

BACKGROUND: The hemodynamics of the cerebral sinuses play a vital role in understanding blood flow-related diseases, yet the hemodynamics of the cerebral sinuses in normal adults remains an unresolved issue. PURPOSE: To evaluate hemodynamics in the cerebral sinus of adults using 4-dimensional flow MRI (4D Flow MRI). STUDY TYPE: Cross-sectional. POPULATION: Ninety-nine healthy volunteers (mean age, 42.88 ± 13.16 years old; females/males, 55/44). FIELD STRENGTH/SEQUENCE: 3 T/4D Flow MRI. ASSESSMENT: The blood flow velocity, average blood flow rate (Q), and vortexes at the superior sagittal sinus (SSS), straight sinus (STS), transverse sinus, sigmoid sinus, and jugular bulb of each volunteer were evaluated by two independent neuroradiologists. The relationship between the total cerebral Q and sex and age was also assessed. Twelve volunteers underwent two scans within a month. STATISTICAL TESTS: The intraclass correlation coefficient (ICC) evaluated the inter-observer agreement. Blood flow parameters among volunteers were compared by the independent-sample t-test or Mann-Whitney U test. The multiple linear regression equation was used to evaluate the relationship between total cerebral Q and age and sex. P < 0.05 indicated statistical significance. RESULTS: The test-retest and interobserver reliability of average velocity and Q were moderate to high (ICC: 0.54-0.99). Cerebral sinus velocity varied by segment and cardiac cycle. The SSS's velocity and Q increased downstream and Q near torcular herophili was 3.5 times that through the STS. The total cerebral Q decreased by 0.06 mL/s per year (ß = -0.06 ± 0.013) and was sex-independent within the group. Vortexes were found in 12.12%, 8.9%, and 59.8% of torcular herophili, transverse-sigmoid junction, and jugular bulb, respectively, and were related to higher upstream flow. DATA CONCLUSION: Cerebral sinuses could be measured visually and quantitatively in vivo by 4D Flow MRI, providing a basis for future research on pulsating tinnitus, multiple sclerosis, and other related diseases. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Prerequisites for Clinical Implementation of Whole-Heart 4D-Flow MRI: A Delphi Analysis.

Whole-heart 4D-flow MRI is a valuable tool for advanced visualization and quantification of blood flow in cardiovascular imaging. Despite advantages over 2D-phase-contrast flow, clinical implementation remains only partially exploited due to many hurdles in all steps, from image acquisition, reconstruction, postprocessing and analysis, clinical embedment, reporting, legislation, and regulation to data storage. The intent of this manuscript was 1) to evaluate the extent of clinical implementation of whole-heart 4D-flow MRI, 2) to identify hurdles hampering clinical implementation, and 3) to reach consensus on requirements for clinical implementation of whole-heart 4D-flow MRI. This study is based on Delphi analysis. This study involves a panel of 18 experts in the field on whole-heart 4D-flow MRI. The experience with and opinions of experts (mean 13 years of experience, interquartile range 6) in the field were aggregated. This study showed that among experts in the cardiovascular field, whole-heart 4D-flow MRI is currently used for both clinical and research purposes. Overall, the panelists agreed that major hurdles currently hamper implementation and utilization. The sequence-specific hurdles identified were long scan time and lack of standardization. Further hurdles included cumbersome and time-consuming segmentation and postprocessing. The study concludes that implementation of whole-heart 4D-flow MRI in clinical routine is feasible, but the implementation process is complex and requires a dedicated, multidisciplinary team. A predefined plan, including risk assessment and technique validation, is essential. The reported consensus statements may guide further tool development and facilitate broader implementation and clinical use. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 5.

Fully Automated Valve Segmentation for Blood Flow Assessment From 4D Flow MRI Including Automated Cardiac Valve Tracking and Transvalvular Velocity Mapping.

BACKGROUND: Automated 4D flow MRI valvular flow quantification without time-consuming manual segmentation might improve workflow. PURPOSE: Compare automated valve segmentation (AS) to manual (MS), and manually corrected automated segmentation (AMS), in corrected atrioventricular septum defect (c-AVSD) patients and healthy volunteers, for assessing net forward volume (NFV) and regurgitation fraction (RF). STUDY TYPE: Retrospective. POPULATION: 27 c-AVSD patients (median, 23 years; interquartile range, 16-31 years) and 24 healthy volunteers (25 years; 12.5-36.5 years). FIELD STRENGTH/SEQUENCE: Whole-heart 4D flow MRI and cine steady-state free precession at 3T. ASSESSMENT: After automatic valve tracking, valve annuli were segmented on time-resolved reformatted trans-valvular velocity images by AS, MS, and AMS. NFV was calculated for all valves, and RF for right and left atrioventricular valves (RAVV and LAVV). NFV variation (standard deviation divided by mean NFV) and NFV differences (NFV difference of a valve vs. mean NFV of other valves) expressed internal NFV consistency. STATISTICAL TESTS: Comparisons between methods were assessed by Wilcoxon signed-rank tests, and intra/interobserver variability by intraclass correlation coefficients (ICCs). P < 0.05 was considered statistically significant, with multiple testing correction. RESULTS: AMS mean analysis time was significantly shorter compared with MS (5.3 ± 1.6 minutes vs. 9.1 ± 2.5 minutes). MS NFV variation (6.0%) was significantly smaller compared with AMS (6.3%), and AS (8.2%). Median NFV difference of RAVV, LAVV, PV, and AoV between segmentation methods ranged from -0.7-1.0 mL, -0.5-2.8 mL, -1.1-3.6 mL, and - 3.1--2.1 mL, respectively. Median RAVV and LAVV RF, between 7.1%-7.5% and 3.8%-4.3%, respectively, were not significantly different between methods. Intraobserver/interobserver agreement for AMS and MS was strong-to-excellent for NFV and RF (ICC ≥0.88). DATA CONCLUSION: MS demonstrates strongest internal consistency, followed closely by AMS, and AS. Automated segmentation, with or without manual correction, can be considered for 4D flow MRI valvular flow quantification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Cluster analysis of 100 Marfan patients based on aortic 4D flow MRI and Z-score: insights into disease heterogeneity and stratification of subgroups.

OBJECTIVES: 4D flow MRI-derived variables from Marfan patients are highly heterogeneous. Our aim was to identify distinct Marfan patient subgroups based on aortic 4D flow MRI and Z-score for stratification of distinct hemodynamic profiles and clinical features by means of hierarchical cluster analysis. MATERIALS AND METHODS: One hundred Marfan patients underwent baseline aortic 4D flow MRI at 3 T. Z-scores, degree of helical and vortical flow, wall shear stress, flow displacement, and peak velocity were determined in the ascending aorta. Sex, age, BMI, antihypertensive medication, and dural ectasia were recorded. Hierarchical cluster analysis was performed using 4D flow MRI variables and Z-scores as input. RESULTS: Cluster analysis resulted in three distinct clusters characterized by different Z-scores (mean ± SD); cluster 1: 0.4 ± 1.1 vs. cluster 2: 3.1 ± 1.1 vs. cluster 3: 3.6 ± 1.9. The three clusters delivered differences in helical and vortical flow patterns (global p = 0.003 and p < 0.001, respectively), wall shear stress (0.49 ± 0.11 vs. 0.44 ± 0.12 vs. 0.37 ± 0.09 N/m2, global p < 0.001), flow displacement (0.11 ± 0.05 vs. 0.16 ± 0.08 vs. 0.15 ± 0.07, global p = 0.006), and peak velocity (76.3 ± 9.0 vs. 60.1 ± 7.3 vs. 56.0 ± 7.8 cm/s, global p < 0.001). Patients in cluster 1 and 2 were relevantly younger than in cluster 3 (32.3 ± 13.8 vs. 32.8 ± 12.6 vs. 40.2 ± 15.0 years, all pairwise ∆p < 0.0297). CONCLUSION: Hierarchical cluster analysis based on aortic 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients, each characterized by specific hemodynamic profiles and clinical features. Follow-up of our patients is warranted to assess if 4D flow MRI- and Z-score-based stratification can predict future aortic diameter growth and ultimately improve outcomes. CLINICAL RELEVANCE STATEMENT: A combination of Z-score and 4D flow MRI-derived parameters may help identify subgroups of Marfan patients representing different stages or phenotypes of aortic disease, which require specific management strategies. KEY POINTS: Four-dimensional (4D) flow MRI-derived variables of Marfan patients are highly heterogeneous across varying Z-scores. Cluster analysis based on 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients. A combination of Z-score and 4D flow MRI-derived parameters may identify different stages of aortic disease in Marfan patients.

Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly. METHODS: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results. RESULTS: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation. CONCLUSIONS: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.

Comprehensive sex-specific and age-dependent analysis of 4D-flow MRI assessed aortic blood flow-related parameters in normal subjects using single-vendor MR systems and single-vendor software.

BACKGROUND: Aortic blood flow characterization by 4D flow MRI is increasingly performed in aneurysm research. A limited number of studies have established normal values that can aid the recognition of abnormal flow at an early stage. This study aims to establish additional sex-specific and age-dependent reference values for flow-related parameters in a large cohort of healthy adults. METHODS: 212 volunteers were included, and 191 volunteers completed the full study protocol. All underwent 4D flow MRI of the entire aorta. Quantitative values for velocity, vorticity, helicity, as well as total, circumferential, and axial wall shear stress [WSS] were determined for the aortic root [AoR], ascending aorta [AAo], aortic arch [AoA], descending [DAo], suprarenal [SRA], and infrarenal aorta [IRA]. Vorticity and helicity were indexed for segment volume (mL). RESULTS: The normal values were estimated per sex- and age-group, where significant differences between males (M) and females (F) were found only for specific age groups. More specifically, the following variables were significantly different after applying the false discovery rate correction for multiple testing: 1) velocity in the AAo and DAo in the 60-70 years age group (mean±SD: (M) 47.0 ± 8.2cm/s vs. (F) 38.4 ± 6.9cm/s, p=0.001 and, (M) 55.9 ± 9.9cm/s vs. (F) 46.5 ± 5.5cm/s, p=0.002), 2) normalized vorticity in AoR in the 50-59 years age group ((M) 27539 ± 5042s-1mL-1 vs. (F) 30849 ± 7285s-1mL-1, p=0.002), 3) axial WSS in the Aao in the 18-29 age group ((M) 1098 ± 203 mPa vs. (F) 921 ± 121 mPa, p=0.002). Good to strong negative correlations with age were seen for almost all variables, in different segments, and for both sexes. CONCLUSION: This study describes reference values for aortic flow-related parameters as acquired by 4D flow MRI. We observed limited differences between males and females. A negative relationship with age was seen for almost all flow-related parameters and segments.

Assessment of abnormal transvalvular flow and wall shear stress direction for pediatric/young adults with bicuspid aortic valve: a cross-sectional 4D flow study.

BACKGROUND: Aortic dilation is seen in pediatric/young adult patients with bicuspid aortic valve (BAV), and hemodynamic markers to predict aortic dilation are necessary for monitoring. Although promising hemodynamic metrics, such as abnormal wall shear stress (WSS) magnitude, have been proposed for adult BAV patients using 4D flow cardiovascular magnetic resonance, those for pediatric BAV patients have less frequently been reported, partly due to scarcity of data to define normal WSS range. To circumvent this challenge, this study aims to investigate if a recently proposed 4D flow-based hemodynamic measurement, abnormal flow directionality, is associated with aortic dilation in pediatric/young adult BAV patients. METHODS: 4D flow scans for BAV patients (<20 years old) and age-matched controls were retrospectively enrolled. Static segmentation for the aorta and pulmonary arteries was obtained to quantify peak systolic hemodynamics and diameters in the proximal aorta. In addition to peak velocity, wall shear stress (WSS), vorticity, helicity, and viscous energy loss, direction of aortic velocity and WSS in BAV patients was compared with that of control atlas using registration technique; angle differences of >60deg and >120deg were defined as moderately and severely abnormal, respectively. Association between the obtained metrics and normalized diameters (Z-scores) were evaluated at the sinotubular junction, mid ascending aorta, and distal ascending aorta. RESULTS: Fifty-three BAV patients, including eighteen with history of repaired aortic coarctation, and seventeen controls were enrolled. Correlation between moderately abnormal velocity/WSS direction and aortic Z-scores was moderate to strong at the sinotubular junction and mid ascending aorta (R=0.62-0.81; p<0.001) while conventional measurements exhibited weaker correlation (|R|=0.003-0.47, p=0.009-0.99) in all subdomains. Multivariable regression analysis found moderately abnormal velocity direction and existence of aortic regurgitation (only for isolated BAV group) were independently associated with mid ascending aortic Z-scores. CONCLUSION: Abnormal velocity and WSS directionality in the proximal aorta was strongly associated with aortic Z-scores in pediatric/young adult BAV patients.

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

BACKGROUND: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies. METHODS: The study population consists of 260 4D flow CMR datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics, such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced three-dimensional U-net convolutional neural network (CNN) architecture with residual units was trained for time-resolved two-dimensional aortic cross-sectional segmentation. Model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation. RESULTS: The representation of technical factors, such as scanner vendor and field strength, in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients' datasets performed well on datasets of healthy subjects but not vice versa. CONCLUSION: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow CMR, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

Four-dimensional flow cardiovascular magnetic resonance aortic cross-sectional pressure changes and their associations with flow patterns in health and ascending thoracic aortic aneurysm.

BACKGROUND: Ascending thoracic aortic aneurysm (ATAA) is a silent and threatening dilation of the ascending aorta (AscAo). Maximal aortic diameter which is currently used for ATAA patients management and surgery planning has been shown to inadequately characterize risk of dissection in a large proportion of patients. Our aim was to propose a comprehensive quantitative evaluation of aortic morphology and pressure-flow-wall associations from four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) data in healthy aging and in patients with ATAA. METHODS: We studied 17 ATAA patients (64.7 ± 14.3 years, 5 females) along with 17 age- and sex-matched healthy controls (59.7 ± 13.3 years, 5 females) and 13 younger healthy subjects (33.5 ± 11.1 years, 4 females). All subjects underwent a CMR exam, including 4D flow and three-dimensional anatomical images of the aorta. This latter dataset was used for aortic morphology measurements, including AscAo maximal diameter (iDMAX) and volume, indexed to body surface area. 4D flow MRI data were used to estimate 1) cross-sectional local AscAo spatial (∆PS) and temporal (∆PT) pressure changes as well as the distance (∆DPS) and time duration (∆TPT) between local pressure peaks, 2) AscAo maximal wall shear stress (WSSMAX) at peak systole, and 3) AscAo flow vorticity amplitude (VMAX), duration (VFWHM), and eccentricity (VECC). RESULTS: Consistency of flow and pressure indices was demonstrated through their significant associations with AscAo iDMAX (WSSMAX:r = -0.49, p < 0.001; VECC:r = -0.29, p = 0.045; VFWHM:r = 0.48, p < 0.001; ∆DPS:r = 0.37, p = 0.010; ∆TPT:r = -0.52, p < 0.001) and indexed volume (WSSMAX:r = -0.63, VECC:r = -0.51, VFWHM:r = 0.53, ∆DPS:r = 0.54, ∆TPT:r = -0.63, p < 0.001 for all). Intra-AscAo cross-sectional pressure difference, ∆PS, was significantly and positively associated with both VMAX (r = 0.55, p = 0.002) and WSSMAX (r = 0.59, p < 0.001) in the 30 healthy subjects (48.3 ± 18.0 years). Associations remained significant after adjustment for iDMAX, age, and systolic blood pressure. Superimposition of ATAA patients to normal aging trends between ∆PS and WSSMAX as well as VMAX allowed identifying patients with substantially high pressure differences concomitant with AscAo dilation. CONCLUSION: Local variations in pressures within ascending aortic cross-sections derived from 4D flow MRI were associated with flow changes, as quantified by vorticity, and with stress exerted by blood on the aortic wall, as quantified by wall shear stress. Such flow-wall and pressure interactions might help for the identification of at-risk patients.

A comprehensive MRI-based computational model of blood flow in compliant aorta using radial basis function interpolation.

BACKGROUND: Properly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall. METHODS: We performed two different types of simulations-static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol. RESULTS: The simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain). CONCLUSIONS: In conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.

Evaluation of left ventricular blood flow kinetic energy in patients with acute myocardial infarction by 4D Flow MRI: a preliminary study.

PURPOSE: To evaluate the intracavity left ventricular (LV) blood flow kinetic energy (KE) parameters using four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) in patients with acute myocardial infarction (AMI). METHODS: Thirty AMI patients and twenty controls were examined via CMR, which included cine imaging, late gadolinium enhancement (LGE) and global heart 4D flow imaging. The KE parameters were indexed to LV end-diastolic volume (EDV) to obtain average, systolic and diastolic KE as well as the proportion of LV in-plane KE (%). These parameters were compared between the AMI patients and controls and between the two subgroups. RESULTS: Analysis of the LV blood flow KE parameters at different levels of the LV cavity and in different segments of the same level showed that the basal level had the highest blood flow KE while the apical level had the lowest in the control group. There were no significant differences in diastolic KE, systolic in-plane KE and diastolic in-plane KE between the anterior wall and posterior wall (p > 0.05), only the systolic KE had a significant difference between them (p < 0.05). Compared with those in the control group, the average (10.7 ± 3.3 µJ/mL vs. 14.7 ± 3.6 µJ/mL, p < 0.001), systolic (14.6 ± 5.1 µJ/mL vs. 18.9 ± 3.9 µJ/mL, p = 0.003) and diastolic KE (7.9 ± 2.5 µJ/mL vs. 10.6 ± 3.8 µJ/mL, p = 0.018) were significantly lower in the AMI group. The average KE in the infarct segment was lower than that in the noninfarct segment in the AMI group (49.5 ± 18.7 µJ/mL vs. 126.3 ± 50.7 µJ/mL, p < 0.001), while the proportion of systolic in-plane KE increased significantly (61.8%±11.5 vs. 42.9%±14.4, p = 0.001). CONCLUSION: The 4D Flow MRI technique can be used to quantitatively evaluate LV regional hemodynamic parameters. There were differences in the KE parameters of LV blood flow at different levels and in different segments of the same level in healthy people. In AMI patients, the average KE of the infarct segment decreased, while the proportion of systolic in-plane KE significantly increased.

Aortic valve area index values of Trifecta implants correlate with energy loss and increased valve stress.

Biological valves are becoming more frequently used in aortic valve replacement. While several reports have evaluated the performance of biological valves, echocardiography studies during exercise stress remain scarce. Furthermore, no current reports compare rate changes in the aortic valve area of biological valves under increased exercise load. Here, we performed exercise stress echocardiography in patients after AVR with Trifecta or Inspiris valves and compared the rates of change in aortic valve areas (AVA). In addition, hydrodynamic analysis at rest was conducted with four-dimensional flow magnetic resonance imaging (4D-flow MRI). Exercise stress echocardiography was performed in seven Trifecta and seven Inspiris patients who underwent AVR at our hospital while 4D flow MRI was performed in all but two Trifecta cases. Comparing the percentage change in AVA when loaded to 25 W versus at rest, Trifecta was greater than Inspiris (28.7 ± 36.0 vs - 0.8 ± 12.4%). The smaller AVA at rest was considered causative for this. Meanwhile, Trifecta systolic energy loss in the prosthetic valve segment on 4D-flow MRI (97.5 ± 35.9 vs 52.7 ± 25.3 mW) was higher than Inspiris. The opening of the Trifecta valve was considered to be restricted at rest and this may reflect the current reports of early valve degradation requiring reoperation. Taken together, we observed that the Trifecta design may promote faster wear due to higher valve stress.

The immediate post-operative impact of infrarenal aortic endografts on renal arterial flow dynamics: Insights from four-dimensional flow magnetic resonance imaging analysis.

OBJECTIVES: This study aims to quantify changes in renal blood flow before and after endovascular aneurysm repair (EVAR) using four-dimensional (4D) flow magnetic resonance imaging (MRI) and evaluate its correlation with renal impairment. METHODS: In this retrospective analysis, 18 patients underwent elective EVAR for infrarenal fusiform abdominal aortic aneurysms using Excluder or Endurant endografts. 4D flow MRI scans were conducted before and 1-4 days after EVAR. Hemodynamics were quantified at the suprarenal aorta (SupAo), bilateral renal arteries (RRA and LRA), and infrarenal aorta (InfAo). Cardiac phase-resolved blood flow values (BFVs), relative flow distribution (RFD), and flow change rates (FCRs) were assessed. Estimated glomerular filtration rate (eGFR) was measured pre- and postoperatively. RESULTS: A total of 16 patients were analyzed after excluding two outliers. Pre-EVAR BFVs were 23.1 ± 8.3, 3.7 ± 1.3, 3.4 ± 1.2, and 15.1 ± 5.9 mL/cycle, while post-EVAR BFVs were 20.9 ± 6.9, 3.8 ± 1.1, 3.2 ± 0.9, and 12.1 ± 4.3 mL/cycle in SupAo, RRA, LRA, and InfAo, respectively. Comparing Excluder (N = 8) and Endurant (N = 8), the total renal FCR was 121.8% [106.6-144.7] versus 101.3% [63.8-121.8] (p = 0.110), suggesting a potential improvement in renal blood flow with the Excluder, although not statistically significant. A significant correlation was found between the total renal FCR and the relative eGFR at 6 months (Spearman correlation coefficient, 0.789; p < 0.001). CONCLUSIONS: The endografts, particularly the Excluder, showed potential in improving renal artery blood flow in some patients. The significant correlation between the total renal FCR and the relative eGFR at 6 months suggests that acute hemodynamic alterations induced by EVAR may impact post-operative renal function. Further research is needed to confirm these findings and assess their clinical implications.

Intracranial aneurysm stiffness assessment using 4D Flow MRI.

BACKGROUND: Although arterial stiffness is known as a biomarker for cardiovascular events and stroke, there is limited information in the literature regarding the stiffness of intracranial aneurysms. In this study, we aim to assess the stiffness of intracranial aneurysms using 4D Flow MRI. METHODS: A total of 27 aneurysms in 25 patients with internal carotid artery aneurysms were included in this study. Using 4D Flow MRI, we measured the arterial pulse wave form during a cardiac cycle at planes proximal and distal to the target aneurysm. The damping of these waveforms through the aneurysm was defined as the aneurysm damping index (ADI) and compared to the contralateral side. We also investigated the clinical factors related to the ADI. RESULTS: ADI assessment was successful in all cases. The average ADI was 1.18±0.28, which was significantly larger than 1.0 (P = 0.0027 [t-test]). The ADI on the aneurysm side was larger than on the contralateral side (1.19±0.30 vs 1.05±0.17, P = 0.029 [t-test]). On multivariate analysis, the use of beta-blockers (ß=0.46, P = 0.015) and smoking history (ß=-0.22, P = 0.024) showed a significant correlation with ADI. CONCLUSION: We have proposed a novel method to observe arterial pulse wave dumping through intracranial aneurysm using 4D Flow MRI. The damping can be quantitatively observed, and the ADI has correlations with clinical factors such as antihypertensive drugs and smoking. Further studies should focus more on evaluating aneurysm stiffness and its clinical applications.

4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.

PURPOSE: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. METHODS: A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. RESULTS: Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 ± 6.77 mmHg) was observed. CONCLUSION: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.

A comparison of phase unwrapping methods in velocity-encoded MRI for aortic flows.

PURPOSE: The phase of a MRI signal is used to encode the velocity of blood flow. Phase unwrapping artifacts may appear when aiming to improve the velocity-to-noise ratio (VNR) of the measured velocity field. This study aims to compare various unwrapping algorithms on ground-truth synthetic data generated using computational fluid dynamics (CFD) simulations. METHODS: We compare four different phase unwrapping algorithms on two different synthetic datasets of four-dimensional flow MRI and 26 datasets of 2D PC-MRI acquisitions including the ascending and descending aorta. The synthetic datasets are constructed using CFD simulations of an aorta with a coarctation, with different levels of spatiotemporal resolutions and noise. The error of the unwrapped images was assessed by comparison against the ground truth velocity field in the synthetic data and dual-VENC reconstructions in the in vivo data. RESULTS: Using the unwrapping algorithms, we were able to remove aliased voxels in the data almost entirely, reducing the L2-error compared to the ground truth by 50%-80%. Results indicated that the best choice of algorithm depend on the spatiotemporal resolution and noise level of the dataset. Temporal unwrapping is most successful with a high temporal and low spatial resolution ( δ t = 30 $$ \delta t=30 $$ ms, h = 2 . 5 $$ h=2.5 $$ mm), reducing the L2-error by 70%-85%, while Laplacian unwrapping performs better with a lower temporal or better spatial resolution ( δ t = 60 $$ \delta t=60 $$ ms, h = 1 . 5 $$ h=1.5 $$ mm), especially for signal-to-noise ratio (SNR) 12 as opposed to SNR 15, with an error reduction of 55%-85% compared to the 50%-75% achieved by the Temporal method. The differences in performance between the methods are statistically significant. CONCLUSIONS: The temporal method and spatiotemporal Laplacian method provide the best results, with the spatiotemporal Laplacian being more robust. However, single- V enc $$ {V}_{\mathrm{enc}} $$ methods only situationally and not generally reach the performance of dual- V enc $$ {V}_{\mathrm{enc}} $$ unwrapping methods.