Bayesian Intraventricular Vector Flow Mapping (BVFM)

Intracardiac blood flow analysis is quickly growing and allows for assessing flow dynamics, vortex formation, cardiac dysfunction, energetic efficiency, and other quantitative biomarkers of cardiovascular health. However, the lack of uncertainty quantification in medical imaging modalities makes it challenging to translate intracardiac blood flow analysis to guide medical decisions. Additionally, current methods used to quantify blood flow in the heart are limited due to assumptions (e.g., flow planarity), and post-processing algorithms rely on input data (e.g., wall segmentations) for boundary conditions that are also prone to uncertainty. Here we present a general flow mapping method rooted in Bayesian inference that allows us to fuse data from different imaging modalities and propagate their respective uncertainties to reconstruct intracardiac flow fields. We use an echocardiographic simulator (MATLAB Ultrasound Toolbox and SIMUS) to explore image quality and the parameter space to test our Bayesian framework. We apply our general framework to synthetic ultrasound data and two different clinical cases: 1) echo-PIV and echo-Doppler fusion and 2) Doppler multiscale fusion.