Multiyear Interval Changes in Aortic Wall Shear Stress in Patients with Bicuspid Aortic Valve Assessed by 4D Flow MRI.

BACKGROUND: In patients with bicuspid aortic valve (BAV), 4D flow MRI can quantify regions exposed to abnormal aortic hemodynamics, including high wall shear stress (WSS), a known stimulus for arterial wall dysfunction. However, the long-term multiscan reproducibility of 4D flow MRI-derived hemodynamic parameters is unknown. PURPOSE: To investigate the long-term stability of 4D flow MRI-derived peak velocity, WSS, and WSS-derived heatmaps in patients with BAV undergoing multiyear surveillance imaging. STUDY TYPE: Retrospective. POPULATION: 20 BAV patients (mean age 48.4 ± 13.9 years; 14 males) with five 4D flow MRI scans, with intervals of at least 6 months between scans, and 125 controls (mean age: 50.7 ± 15.8 years; 67 males). FIELD STRENGTH/SEQUENCE: 1.5 and 3.0T, prospectively ECG and respiratory navigator-gated aortic 4D flow MRI. ASSESSMENT: Automated AI-based 4D flow analysis pipelines were used for data preprocessing, aorta 3D segmentation, and quantification of ascending aorta (AAo) peak velocity, peak systolic WSS, and heatmap-derived relative area of elevated WSS compared to WSS ranges in age and sex-matched normative control populations. Growth rate was derived from the maximum AAo diameters measured on the first and fifth MRI scans. STATISTICAL TESTS: One-way repeated measures analysis of variance. P < 0.05 indicated significance. RESULTS: One hundred 4D flow MRI exams (five per patient) were analyzed. The mean total follow-up duration was 5.5 ± 1.1 years, and the average growth rate was 0.3 ± 0.2 mm/year. Peak velocity, peak systolic WSS, and relative area of elevated WSS did not change significantly over the follow-up period (P = 0.64, P = 0.69, and P = 0.35, respectively). The patterns and areas of elevated WSS demonstrated good reproducibility on semiquantitative assessment. CONCLUSION: 4D flow MRI-derived peak velocity, WSS, and WSS-derived heatmaps showed good multiyear and multiscan stability in BAV patients with low aortic growth rates. These findings underscore the reliability of these metrics in monitoring BAV patients for potential risk of dilation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

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.