Bayesian Segmentation of 4D Flow MRI Data Based on Flow Physics*

4D flow MRI provides time-resolved, 3-directional measurements of cardiovascular flow in vivo. Improving the accuracy and repeatability of 4D flow data segmentation is critical for reliable assessment of hemodynamic metrics associated with cardiovascular disease, e.g., wall shear stress. We propose a Bayesian vessel segmentation algorithm that generates time-resolved volumetric masks of vessels by assessing the probability of fluid flow in each voxel and time frame. The flow likelihood is assessed using the normalized divergence, defined as the velocity divergence non-dimensionalized using the mean speed and the voxel size. The Bayesian vessel segmentation algorithm’s performance was demonstrated on in vivo 4D flow MRI measurements in the aorta of three patients. Manual segmentation is used as the benchmark for evaluating the performance of the proposed Bayesian vessel segmentation method. We quantify the performance of the vessel segmentation algorithm using the sensitivity metric computed as the ratio of true positives to the total number of manually segmented voxels. The sensitivity values ranged from 95.6% to ­­99.9% across all patients, with a root mean square of ­­98.4%. Efforts are ongoing to apply the Bayesian segmentation algorithm to different vascular territories.