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

项目总结： 这项提议旨在发展高级机器学习和人工智能(ML/AI) 快速、准确地识别晚期机械激活(LMA)和 用奇异值分解(CURE-SVD)计算环向一致性估计 标准电影心脏磁共振(CMR)图像。我们的长远目标是发展 可以根据在以下位置获取的电影图像自动确定LMA位置/CURE-SVD的网络 全球任何CMR设施，从而满足有效指导设备的关键需求- 以心脏再同步治疗(CRT)等为基础的疗法，潜在地治疗数百万心脏 失败的病人。为了实现这一目标，我们将使用我们拥有的丰富且唯一的数据集 在我们机构收集了200多名接受CRT的患者，并进行了中位随访 五年了。该数据集包括来自EHR审查的人口统计数据和共病疾病， 使用CMR电影/高密度/LGE(晚期Gd增强)的CRT/后CRT成像， 超声心动图和基于总存活率的多维反应参数、血清 B型利钠肽检测、生活质量调查问卷和运动试验中的VO2峰值。 这个提议的中心假设是，这些ML/AI方法将有效地识别 来自电影成像的无疤痕LMA部位的特征，获得了极好的一致性 与原来基于密集的评估相比，并预测CRT后的结果。我们的 具体目标是(I)通过发展联合神经来识别LMA位置和计算治愈-SVD 使用来自电影SSFP/GRE图像的输入的网络，(Ii)在 网络中，我们将开发一种新颖的多任务学习方法，以考虑伤疤信息中的 确定无疤痕的LMA位置，以及(Iii)比较我们建议的 地面实况稠密和商业特征跟踪结果的方法 在200多名完全CRT患者的数据集中预测CRT结果的软件 生存和心律失常结果的反应数据和长期随访。

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.