4D flow MRI error analysis and segmentation using principles of fluid dynamics

4D flow MRI measurements of blood flow are hindered by limited resolution, partial volume effects, and noise, affecting the accuracy of hemodynamic metrics related to vessel remodeling and vascular disease. Resolution limitations and noise also make the segmentation of 4D flow MRI data labor-intensive and challenging. Recently developed machine-learning methods require large training datasets and are limited to training-specific vasculature, disease conditions, and imaging systems. We develop automatic segmentation and error analysis methods for 4D flow MRI that are based on statistical inference, fluid dynamics principles, and imaging physics. In this approach, the flow-containing voxels are segmented by identifying net flow effects using the standardized difference of means (SDM) velocity. The error analysis of 4D flow-measured velocities is performed by assessing the local bias error of the measurement due to the intra-voxel distribution and quantifying the measurement uncertainty based on the local velocity error variance and error correlations. The algorithms are tested on synthetic 4D flow datasets, in vitro 4D flow data acquired in 3D printed phantoms, and in vivo 4D flow data in cerebral and thoracic vessels. 4D flow MRI error analysis enables quantitative comparison of datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.