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

使用 Dual-venc 4D 流 MRI 和深度学习对二尖瓣反流进行量化

• 批准号: 10648495
• 负责人: Jeesoo Lee
• 金额: $ 20万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10648495
• 关键词: 3-Dimensional; 4D MRI; Acceleration; Address; Affect; Agreement; Area; Blood Flow Velocity; Blood flow; Cardiac; Cessation of life; Classification; Clinical; Compensation; Complex; Coupled; Data; Data Set; Detection; Disease; Echocardiography; Enrollment; Evaluation; Heart; Heart failure; Image; In Vitro; Learning; Left atrial structure; Lesion; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Methods; Mitral Valve Insufficiency; Modeling; Morphology; Nature; Noise; Output; Patients; Phase; Physiologic pulse; Play; Population; Process; Pulmonary valve structure; Reporting; Reproducibility; Resolution; Risk; Role; Scanning; Scheme; Severities; Severity of illness; Shunt Device; Site; Stroke Volume; Techniques; Testing; Time; Training; Tricuspid valve structure; Uncertainty; Visualization; Work; accurate diagnostics; aortic valve; cardiac magnetic resonance imaging; clinical application; clinical translation; deep learning; design; detection method; healthy volunteer; hemodynamics; high risk; imaging modality; improved; in vivo; innovation; learning network; multitask; novel; patient stratification; research clinical testing; risk stratification; success; three-dimensional visualization; tool; translational approach; treatment planning

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.

项目摘要/摘要 二尖瓣反流（MVR）是影响美国5％以上的最常见瓣膜疾病之一 人口。及时，准确地评估MVR对这些患者至关重要，因为MVR会随着时间的流逝而恶化 未经治疗的严重MVR显着增加了心力衰竭和死亡的风险。目前，超声心动图 （回声）是MVR的主要成像方式，其中MVR流量的定量在 确定疾病的严重程度。但是，继承了Echo的弱点（2D采集，1方向速度 测量值）由于复杂的MVR血液动力学而限制了数量的精度，其特征为高 速度（4-6 m/s），异质性（偏心/多/非静电射流）流动射流，动态变化 二尖瓣的形态。心脏MRI（CMR）可用于间接量化基于MVR流量 然而 由于减法，并且在分流流和/或多个瓣膜病变的患者中不适用。更远， 始终报道了CMR和ECHO之间的不一致性，这表明需要准确和 可靠的定量技术。 4D流MRI提供了对4D（3D+时间）内部血流的独特访问，从而实现“直接”定量 MVR Jet FLSOW动力学没有常规回声和基于CMR的方法的限制。但是，临床 这种方法的翻译仍然是挑战，原因有两个。一个是高速编码灵敏度 传统的单胎4D流MRI需要（Venc）为4-6 m/s，以捕获高峰值MVR流动速度。 这限制了4D流MRI的速度动态范围，从而导致流量可视化不良并增加 流量定量不确定性。另一个是后处理需要手动和繁琐的检测 在心脏周期，平面放置和射流轮廓上，MVR流量在3D全心中以许多时间框架 限制测量可重复性。该建议旨在通过开发快速双重 - Venc 4D流MRI技术针对MVR流速度采集优化，其次，深度学习技术 用于检测和分割4D MVR流动机以完全自动化MVR流量定量过程。这 特定目标是：（1）使用视野内脉冲MVR流射流模型优化CS Dual-Venc 4D流MRI， （2）验证60名MVR患者中的双元素4D流MRI对回声和CMR验证。 （3）开发一个深度学习网络以完全自动化MVR流量定量管道。 该项目将生成一种可再现和准确的定量方法，用于MVR的临床评估。我们的 框架在成像，深度学习和临床应用方面享有多种创新。从中学到的教训 这应该适用于其他瓣膜反流病变的数量，因此大大扩展了影响 这项工作。