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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要二尖瓣反流 (MVR) 是最常见的瓣膜疾病之一，影响超过 5% 的美国人口。及时、准确的 MVR 评估对于这些患者至关重要，因为 MVR 会随着时间的推移而恶化。目前，未经治疗的严重 MVR 会显着增加心力衰竭和死亡的风险。（回波）是 MVR 的主要成像方式，其中 MVR 流量的定量在然而，回波的固有弱点（2D 采集、1 向速度）测量）由于复杂的 MVR 血流动力学特征而限制了定量精度速度 (4-6 m/s)、异质（偏心/多个/非全收缩射流）动态变化的射流二尖瓣口形态可用于基于心脏 MRI (CMR) 间接量化 MVR 流量。在不同部位测量的每搏输出量存在差异，但是每次测量的误差都会被放大由于减法，不适用于有分流和/或多发瓣膜病变的患者。 CMR 和回波之间的不一致一直表明需要准确和可靠的定量技术。 4D 血流 MRI 提供了对 4D（3D+时间）心脏内血流的独特访问，从而能够“直接”量化 MVR jet flSow 动力学不受传统基于回波和 CMR 的方法的限制，但是在临床上。这种方法的翻译仍然具有挑战性，原因有二：其一是高速编码灵敏度。传统的单 venc 4D 流 MRI 需要 4-6 m/s 的 (venc) 来捕获高峰值 MVR 流射流速度。这限制了 4D 血流 MRI 的速度动态范围，从而导致血流可视化效果不佳并增加了另一个是后处理需要手动且繁琐的检测。心动周期中 3D 全心脏中的 MVR 流射流、多个时间范围内的平面放置和射流轮廓该提案旨在通过开发快速双通道来解决这些限制。 venc 4D 血流 MRI 技术针对 MVR 流速采集进行了优化，其次是深度学习技术用于 4D MVR 射流的检测和分割，以完全自动化 MVR 流量量化过程。具体目标是：（1）使用体外脉冲 MVR 流射流模型优化 CS 双 venc 4D 流 MRI， (2) 根据当天获得的回波和 CMR 验证 60 名 MVR 患者的双静脉 4D 血流 MRI (3) 开发深度学习网络以完全自动化 MVR 流量量化管道。该项目将为 MVR 的临床评估提供可重复且准确的定量方法。该框架在成像、深度学习和临床应用方面享有多项创新。这应该适用于其他瓣膜反流病变的量化，从而大大扩大了影响这项工作的。