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

项目摘要/摘要 二尖瓣返流(MVR)是最常见的瓣膜疾病之一，影响美国超过5%的人。 人口。及时和准确地评估MVR对这些患者至关重要，因为MVR会随着时间的推移而恶化 而未经治疗的严重MVR显著增加了心力衰竭和死亡的风险。目前，超声心动图 (ECHO)是MVR的主要成像方式，其中MVR血流的定量在 确定疾病的严重程度。然而，回波的固有弱点(2D采集、单向速度 测量)限制了量化精度，因为复杂的MVR血流动力学特征是高度的 速度(4-6m/S)、非均质(偏心/多股/非全息)射流及其动态变化 二尖瓣口形态。心脏MRI(CMR)可用于间接量化MVR血流量 然而，在不同位置测量的每搏量的差异，每个测量中的误差被放大 由于减影，不适用于有分流和/或多个瓣膜病变的患者。此外， CMR和ECHO之间的不一致一直被报道，这表明需要准确和 可靠的定量技术。 4D Flow MRI提供了对4D(3D+时间)心内血流的独特访问方式，从而实现了对 MVR JET flSow动力学不受传统回声和基于CMR的方法的限制。然而，临床上 这种方法的翻译仍然具有挑战性，原因有两个。其一是高速编码的敏感性 常规的单静脉4D Flow MRI需要4~6m/S的VENC才能捕捉到较高的峰值MVR血流速度。 这限制了4D Flow MRI的速度动态范围，从而导致较差的流动显示和增加 流量量化的不确定性。另一种是后处理需要人工和繁琐的检测 心动周期内3D全心内的MVR流动射流，多个时间范围内的平面布置和射流轮廓 限制测量的重复性。这项提议寻求通过开发快速的双重- Venc 4D Flow MRI技术针对MVR血流速度采集进行了优化，其次是一种深度学习技术 用于4D MVR流动射流的检测和分割，以完全自动化MVR流动量化过程。这个 具体目标是：(1)使用体外脉动MVR流动射流模型优化CS Dual Venc 4D Flow MRI， (2)对60例MVR患者的双VINC 4D Flow磁共振成像与同一天获得的ECHO和CMR进行对比分析。 (3)开发深度学习网络，实现MVR流量量化管道的全自动化。 该项目将为MVR的临床评估提供一种重复性好、准确性高的定量方法。我们的 框架在成像、深度学习和临床应用方面都有多方面的创新。从以下方面吸取的教训 这应该适用于其他瓣膜返流性病变的量化，从而大大扩大了影响 这部作品的价值。

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.