The effects of resolution and segmentation on the accuracy and precision of the WSS algorithm were quantified. We were able to calculate volumetric WSS in the carotid bifurcation and the aorta.

Purpose To compute cohort-averaged wall shear stress (WSS) maps in the thoracic aorta of patients with aortic dilatation or valvular stenosis and to detect abnormal regional WSS. Methods Systolic WSS vectors, estimated from 4D flow MRI data, were calculated along the thoracic aorta lumen in 10 controls, 10 patients with dilated aortas and 10 patients with aortic valve stenosis. 3D segmentations of each aorta were co-registered by group and used to create a cohort-specific aortic geometry. The WSS vectors of each subject were interpolated onto the corresponding cohort-specific geometry to create cohort-averaged WSS maps. A Wilcoxon rank sum test was used to generate aortic P-value maps (P<0.05) representing regional relative WSS differences between groups. Results Cohort-averaged systolic WSS maps and P-value maps were successfully created for all cohorts and comparisons. The dilation cohort showed significantly lower WSS on 7% of the ascending aorta surface, whereas the stenosis cohort showed significantly higher WSS aorta on 34% the ascending aorta surface. Conclusions The findings of this study demonstrated the feasibility of generating cohort-averaged WSS maps for the visualization and identification of regionally altered WSS in the presence of disease, as compared to healthy controls.

There exists considerable controversy surrounding the timing and extent of aortic resection for patients with BAV disease. Since abnormal wall shear stress (WSS) is potentially associated with tissue remodeling in BAV-related aortopathy, we propose a methodology that creates patient-specific ‘heat maps’ of abnormal WSS, based on 4D flow MRI. The heat maps were created by detecting outlier measurements from a volumetric 3D map of ensemble-averaged WSS in healthy controls. 4D flow MRI was performed in 13 BAV patients, referred for aortic resection and 10 age-matched controls. Systolic WSS was calculated from this data, and an ensemble-average and standard deviation (SD) WSS map of the controls was created. Regions of the individual WSS maps of the BAV patients that showed a higher WSS than the mean+1.96SD of the ensemble-average control WSS map were highlighted. Elevated WSS was found on the greater ascending aorta (35% ± 15 of the surface area), which correlated significantly with peak systolic velocity (R2=0.5, P=0.01) and showed good agreement with the resected aortic regions. This novel approach to characterize regional aortic WSS may allow clinicians to gain unique insights regarding the heterogeneous expression of aortopathy and may be leveraged to guide patient-specific resection strategies for aorta repair.

Wall shear stress (WSS) is involved in many pathophysiological processes related to cardiovascular diseases, and knowledge of WSS may provide vital information on disease progression. WSS is generally quantified with computational fluid dynamics (CFD), but can also be calculated using phase contrast MRI (PC-MRI) measurements. In this study, our objectives were to calculate WSS on the entire luminal surface of human carotid arteries using PC-MRI velocities (WSSMRI ) and to compare it with WSS based on CFD (WSSCFD ). Six healthy volunteers were scanned with a 3 T MRI scanner. WSSCFD was calculated using a generalized flow waveform with a mean flow equal to the mean measured flow. WSSMRI was calculated by estimating the velocity gradient along the inward normal of each mesh node on the luminal surface. Furthermore, WSS was calculated for a down-sampled CFD velocity field mimicking the MRI resolution (WSSCFDlowres ). To ensure minimum temporal variation, WSS was analyzed only at diastole. The patterns of WSSCFD and WSSMRI were compared by quantifying the overlap between low, medium and high WSS tertiles. Finally, WSS directions were compared by calculating the angles between the WSSCFD and WSSMRI vectors. WSSMRI magnitude was found to be lower than WSSCFD (0.62 ± 0.18 Pa versus 0.88 ± 0.30 Pa, p < 0.01) but closer to WSSCFDlowres (0.56 ± 0.18 Pa, p < 0.01). WSSMRI patterns matched well with those of WSSCFD. The overlap area was 68.7 ± 4.4% in low and 69.0 ± 8.9% in high WSS tertiles. The angles between WSSMRI and WSSCFD vectors were small in the high WSS tertiles (20.3 ± 8.2°), but larger in the low WSS tertiles (65.6 ± 17.4°). In conclusion, although WSSMRI magnitude was lower than WSSCFD , the spatial WSS patterns at diastole, which are more relevant to the vascular biology, were similar. PC-MRI-based WSS has potential to be used in the clinic to indicate regions of low and high WSS and the direction of WSS, especially in regions of high WSS.

WSS patterns can be estimated based on PC-MRI data in in vitro and in vivo aneurysm geometries. Similar WSS directions as CFD can be discerned.

IntroductionWall shear stress (WSS) and oscillatory shear index (OSI) are associated with atherosclerotic disease. Both parameters are derived from blood velocities, which can be measured with phase-contrast MRI (PC-MRI). Limitations in spatiotemporal resolution of PC-MRI are known to affect these measurements. Our aim was to investigate the effect of spatiotemporal resolution using a carotid artery phantom.MethodsA carotid artery phantom was connected to a flow set-up supplying pulsatile flow. MRI measurement planes were placed at the common carotid artery (CCA) and internal carotid artery (ICA). Two-dimensional PC-MRI measurements were performed with thirty different spatiotemporal resolution settings. The MRI flow measurement was validated with ultrasound probe measurements. Mean flow, peak flow, flow waveform, WSS and OSI were compared for these spatiotemporal resolutions using regression analysis. The slopes of the regression lines were reported in %/mm and %/100ms. The distribution of low and high WSS and OSI was compared between different spatiotemporal resolutions.ResultsThe mean PC-MRI CCA flow (2.5±0.2mL/s) agreed with the ultrasound probe measurements (2.7±0.02mL/s). Mean flow (mL/s) depended only on spatial resolution (CCA:-13%/mm, ICA:-49%/mm). Peak flow (mL/s) depended on both spatial (CCA:-13%/mm, ICA:-17%/mm) and temporal resolution (CCA:-19%/100ms, ICA:-24%/100ms). Mean WSS (Pa) was in inverse relationship only with spatial resolution (CCA:-19%/mm, ICA:-33%/mm). OSI was dependent on spatial resolution for CCA (-26%/mm) and temporal resolution for ICA (-16%/100ms). The regions of low and high WSS and OSI matched for most of the spatiotemporal resolutions (CCA:30/30, ICA:28/30 cases for WSS; CCA:23/30, ICA:29/30 cases for OSI).ConclusionWe show that both mean flow and mean WSS are independent of temporal resolution. Peak flow and OSI are dependent on both spatial and temporal resolution. However, the magnitude of mean and peak flow, WSS and OSI, and the spatial distribution of OSI and WSS did not exhibit a strong dependency on spatiotemporal resolution.

Purpose To investigate the reproducibility and inter-observer variability of 3D aortic velocity vector fields and wall shear stress (WSS) averaged over five systolic timeframes derived from non-contrast 4D-flow-MRI. Methods Fourteen controls underwent test-retest 4D-flow-MRI examinations separated by 16±3 days (resolution=3.0–3.6×2.3–2.6×2.5–2.7mm3; TE/TR/FA=2.5ms/4.9ms/7°; Venc=150cm/s). Two observers was segmented the aorta, and WSS was calculated for both series of scans and both segmentations. Test-retest and inter-observer velocity and WSS vectors were compared on a voxel-by-voxel basis in the aorta and on a regional basis by subdividing the aortas in six segments. Results Test-retest: voxel-by-voxel Bland-Altman analysis revealed small differences (−0.03/−0.02 m/s/Pa), limits of agreement of 0.25 m/s/0.29 Pa and coefficients of variation (CV) of 20% for velocity/WSS. Voxel-by-voxel orthogonal regression analysis showed moderate agreement (Slope: 1.14/1.16, Intraclass Correlation Coefficient (ICC): 0.76/0.67 for velocity/WSS). The regional analysis revealed a CV of 9%/8% and ICC of 0.9/0.9 for velocity/WSS. Inter-observer: voxel-by-voxel difference for WSS was 0, LOA: 0.17/0.19 Pa, CV: 12/13%, slope: 1.01/1.09, ICC: 0.87/0.85 for test/retest. The CV/ICC for WSS in the regional analysis was 4%/1.0 for test and 3%/1.0 for retest. Conclusions Systolic velocity and WSS derived from 4D flow MRI are reproducible between consecutive visits, with low inter-observer variability in healthy volunteers.

Purpose To investigate age-related changes in peak systolic aortic 3D velocity and wall shear stress (WSS) in healthy controls and to investigate the importance of age-matching for 3D mapping of abnormal aortic hemodynamics in bicuspid aortic valve disease (BAV). Methods 4D flow MRI (fields strengths=1.5 – 3T; resolution=2.2–3.9x1.7–2.6x2.2–4.0mm3; venc=150–250cm/s; TE/TR/FA=2.3–2.8ms/4.7–5.4ms/7–15°) was performed in 56 controls (age range: 19–78 years) and in two BAV patient groups each consisting of 10 subjects (group 1: 20–29 years, group 2: 52–57 years). Heat maps showing abnormal 3D velocity and WSS were created for the BAV patients by comparison with an age-matched and with an unmatched control group. The fraction of the aorta exposed to abnormal velocity/WSS was calculated relative to the total aortic volume/surface. Results Significant inverse relationships between age and healthy velocity/WSS were found (R2=0.32/0.39, P<0.001). For BAV group 1, abnormally elevated velocity/WSS was overestimated when compared with older controls (51–60 years) than when correctly age-matched (~25±14% vs. ~8±5%). For BAV group 2, abnormally decreased velocity/WSS was overestimated when compared with younger controls (21–30 years) than when correctly age-matched (~9±7% vs. 1±1%). Conclusion Significant correlations exist between age and peak systolic velocity and WSS. Therefore, robust age-matching is important when creating abnormal 3D aortic velocity and WSS maps for patients with BAV.