Deep Learning To Automate Late Mechanical Activation Detection From Cardiac Magnetic Resonance Images

深度学习自动检测心脏磁共振图像的晚期机械激活

• 批准号: 10593788
• 负责人: Miaomiao Zhang
• 金额: $ 22.37万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593788
• 关键词: Address; Adoption; Agreement; Arrhythmia; Artificial Intelligence; Brain natriuretic peptide; Cardiac; Characteristics; Cicatrix; Computer software; Computers; Consumption; Data; Data Set; Defibrillators; Detection; Devices; Dimensions; Disease; Echocardiography; Educational process of instructing; Effectiveness; Electronics; Exercise Test; Gadolinium; Goals; Grant; Heart; Heart failure; Image; Implant; Inferior; Institution; Joints; Knowledge; Lead; Left; Life; Location; Longterm Follow-up; Machine Learning; Magnetic Resonance; Maps; Measures; Mechanics; Methods; Modeling; Morbidity - disease rate; Myocardial; Network-based; Outcome; Pacemakers; Patient Selection; Patients; Performance; Procedures; Quality of life; Questionnaires; Selection for Treatments; Serum; Site; Specialist; Techniques; Testing; Time; Tissue Viability; Ventricular; Visual; Work; artificial intelligence method; cardiac implant; cardiac magnetic resonance imaging; cardiac resynchronization therapy; comorbidity; deep learning; deep neural network; demographics; design; experience; follow-up; functional restoration; heart function; heart rhythm; imaging study; implantable device; improved; innovation; machine learning method; mortality; multi-task learning; neural network; novel; novel strategies; response; therapy outcome; tool; treatment response

Project summary: This proposal aims to develop advanced machine learning and artificial intelligence (ML/AI) techniques to rapidly and accurately identify sites with late mechanical activation (LMA) and compute circumferential uniformity estimate with singular value decomposition (CURE-SVD) from standard cine cardiac magnetic resonance (CMR) images. Our long-term goal is to develop networks that can determine LMA sites / CURE-SVD automatically from cine images acquired at any CMR facility worldwide, thereby addressing a critical need in the effective guidance of device- based therapies, such as Cardiac resynchronization therapy (CRT), for potentially millions of heart failure patients. To accomplish this goal, we will make use of a rich and unique dataset we have assembled at our institution based on over 200 patients undergoing CRT with a median follow-up of five years. The data set includes demographics and comorbid diseases from EHR review, pre- CRT/post-CRT imaging with CMR cine/DENSE/LGE (late gadolinium enhancement), echocardiography, and multidimensional response parameters based on overall survival, serum B-type natriuretic peptide testing, quality of life questionnaires, and exercise testing for peak VO2. The central hypothesis of this proposal is that these ML/AI methods will effectively identify the characteristics of scar-free LMA sites from cine imaging, achieving excellent agreement compared with the original DENSE-based assessments, and predict post-CRT outcomes. Our specific aims are (i) identifying LMA sites and computing CURE-SVD by developing joint neural networks with inputs from cine SSFP/GRE images, (ii) with the addition of scar from LGE in the network, we will develop a novel multi-task learning to consider scar information in the determination of LMA sites free of scar, and (iii) comparing the performance of our proposed methods with ground truth DENSE and results obtained from commercial feature tracking software to predict CRT outcomes in the dataset with 200+ CRT patients with complete CRT response data and long-term follow-up for survival and arrhythmia outcomes.

项目摘要： 该建议旨在开发先进的机器学习和人工智能（ML/AI） 快速准确地识别具有晚期机械激活（LMA）和 通过从奇异值分解（CURE-SVD）计算圆周均匀性估计值 标准的Cine心脏磁共振（CMR）图像。我们的长期目标是发展 可以自动从从CINE图像中获取的网络 / CURE-SVD来确定LMA站点 / CURE-SVD的网络 全球任何CMR设施，从而在设备的有效指导中满足了至关重要的需求 - 基于心脏重新同步疗法（CRT）等基于基于的疗法 失败患者。为了实现这一目标，我们将利用一个我们拥有的丰富而独特的数据集 根据200多名接受CRT的患者和中位随访的患者，在我们的机构组装 五年。数据集包括EHR评论中的人口统计学和合并症， 用CMR Cine/Cine/致密/LGE（Gadolinium升高），CRT/CRT成像， 超声心动图和基于整体生存的多维响应参数 B型Natriate肽测试，生活质量问卷和峰值VO2的运动测试。 该提议的核心假设是这些ML/AI方法将有效地确定 来自电影成像的无疤痕LMA站点的特征，达成了极好的一致性 与原始的基于密集的评估相比，并预测了CRT后的结果。我们的 特定目的是（i）通过开发关节神经来识别LMA站点和计算CURE-SVD 来自Cine SSFP/GRE图像的输入的网络，（ii）在LGE中添加了疤痕 网络，我们将开发一种新颖的多任务学习，以考虑在 LMA站点没有疤痕的确定，以及（iii）比较我们提议的性能 地面真相密集的方法，并从商业功能跟踪获得的结果 具有200多名CRT患者的数据集中预测CRT结果的软件 响应数据和生存和心律不齐结局的长期随访。