Mitral Regurgitation Quantification Using Dual-venc 4D flow MRI and Deep learning

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Project Summary/Abstract Mitral valvular regurgitation (MVR) is one of the most common valvular diseases affecting over 5% of the U.S. population. Timely and accurate assessment of MVR is crucial for these patients since MVR worsens over time and untreated severe MVR significantly increases risk of heart failure and death. Currently, echocardiography (echo) is the mainstay imaging modality for MVR where quantitation of MVR flow plays an instrumental role in determining disease severity. However, inherent weaknesses of echo (2D acquisition, 1-directional velocity measurements) limit quantification precision due to complex MVR hemodynamics characterized as a high- velocity (4-6 m/s), heterogeneous (eccentric/multiple/non-holosystolic jets) flow jets with dynamically changing mitral orifice morphology. Cardiac MRI (CMR) can be used to indirectly quantify MVR flow volume based on differences in stroke volumes measured at different sites, however, errors in each measurement are amplified due to subtraction and is inapplicable in patients with shunt flows and/or multiple valvular lesions. Further, discordance between CMR and echo has consistently been reported suggesting a need for an accurate and reliable quantitative technique. 4D flow MRI provides unique access to 4D (3D+time) intra-cardiac blood flow enabling “direct” quantification of MVR jet flSow dynamics free from limitations in conventional echo- and CMR-based methods. However, clinical translation of this approach remains challenging for two reasons. One is that a high velocity encoding sensitivity (venc) of 4-6 m/s is required for conventional single-venc 4D flow MRI to capture high peak MVR flow jet velocity. This limits velocity dynamic range of 4D flow MRI and thus, resulting in poor flow visualization and increased flow quantification uncertainty. The other is that post-processing requires manual and cumbersome detection of MVR flow jet in a 3D whole heart over a cardiac cycle, plane placement and jet contouring over many timeframes limiting measurement reproducibility. This proposal seeks to address these limitations by developing a fast dual- venc 4D flow MRI technique optimized for MVR flow velocity acquisition and second, a deep learning technique for detection and segmentation of 4D MVR flow jet to fully automate MVR flow quantification process. The specific objectives are: (1) to optimize CS dual-venc 4D flow MRI using in-vitro pulsatile MVR flow jet models, (2) to validate the dual-venc 4D flow MRI in 60 MVR patients against echo and CMR acquired on the same-day and (3) to develop a deep learning network to fully automate MVR flow quantification pipeline. This project will generate a reproducible and accurate quantitative approach for clinical evaluation of MVR. Our framework enjoys multiple innovations in imaging, deep learning, and clinical application. Lessons learned from this should be applicable to quantification of other valvular regurgitant lesions, thus greatly expanding the impact of this work.