Improved Imaging of Fibrosis in Atrial Fibrillation

项目摘要 在美国，心房颤动（AF）是成人中第二常见的心血管疾病，仅次于 高血压，影响全球3300万人，其中美国有700多万患者， 在欧洲，美国每年有超过40万人入院，每年有500万人就诊，276，000人急诊 每年的客房访问量，美国医疗保健费用每年增加260亿美元，增加了五倍，几乎增加了两倍， 中风和死亡的风险分别增加一倍。疾病过程中的一个关键标志是 左心房（LA）中的胶原和纤维化形成。磁共振成像方法已经开发出来，以评估 左心房，包括纤维化，但仍然是不可靠的和有争议的。具体来说，晚期钆 增强（LGE）MRI作为用于体内可视化的非侵入性成像工具发挥了关键作用， 心房纤维化的定量。然而，不一致的图像质量和大量的专家- 在纤维化量化过程中涉及的水平监督是其再现性的主要障碍。这 该提案提供了一种新的MRI成像技术和相关的机器学习方法，以更好地评估 左心房，并确定这些测量的可重复性。目标1将引入鲁棒性和 用于在LGE的固定时间内获得各向同性分辨率的可重复MRI采集和重建方法 左心房的图像。独特的运动补偿模型将被集成到重建中 LGE数据。Aim 2将提供基于机器学习的精确方法，用于分割左侧 心房壁和评估心房肌纤维化。目标3将严格测试和进一步完善非 实验性犬纤维化模型中的侵袭性纤维化评估方法。目标4将量化 与现有方法相比，新的采集、重建和后处理方法的重现性 患者的方法。该提案将直接影响心脏病学的临床科学和护理标准， 通过显著改善左心房纤维化的LGE成像进行AF管理。技术 这项工作的贡献将进一步影响生物医学图像分析领域，通过改善 基于MRI的心脏组织结构成像技术。为了提高透明度和可重复性， 建议的努力将作为符合可重复研究原则的开源工具发布 开放的科学实践。这种开源性质将进一步使该项目成为未来的催化剂 方法创新和临床研究。我们的长期目标是精简和标准化 心房纤维化量化用于房颤患者的临床管理，本项目将建立 为实现这一目标奠定基础。

Project Summary In the United States, atrial fibrillation (AF) is the second most common cardiovascular condition in adults, after hypertension, affecting 33 million individuals worldwide, with more than 7 million patients in the U.S. and Europe, over 400,000 U.S. hospital admissions per year, 5 million office visits per year, 276,000 emergency room visits per year, a $26 billion annual increase in U.S. healthcare costs, and a five-fold and an almost two- fold increase in the risk of stroke and mortality, respectively. A key marker in the disease process is the collagen and fibrosis formation in the left atrium (LA). MR imaging methods have been developed to assess the left atrium, including fibrosis but are still unreliable and controversial. Specifically, late gadolinium enhancement (LGE) MRI has played a key role as a non-invasive imaging tool for in vivo visualization and quantification of atrial fibrosis. However, the inconsistent image quality and the significant amount of expert- level supervision involved in the fibrosis quantification process are major roadblocks to its reproducibility. This proposal offers a new MRI imaging technique and an associated machine learning approach to better assess the left atrium and to determine the repeatability of these measurements. Aim 1 will introduce robust and repeatable MRI acquisition and reconstruction methods for obtaining isotropic resolution in a fixed time of LGE images of the left atrium. Unique motion compensation models will be integrated into the reconstruction methods for the LGE data. Aim 2 will provide accurate machine learning based methods for segmenting the left atrium wall and assessing atrial myocardium fibrosis. Aim 3 will rigorously test and further refine the non- invasive fibrosis assessment methods in an experimental canine model with fibrosis. Aim 4 will quantify the reproducibility of the new acquisition, reconstruction, and post-processing methods compared to existing methods in patients. This proposal will directly impact clinical science and standards of care in cardiology and AF management through significant improvements to LGE imaging of left atrial fibrosis. Technological contributions of this work will further impact the field of biomedical image analysis through the improvement of MRI-based techniques for imaging cardiac tissue structure. To promote transparency and reproducibility, the proposed efforts will be released as open-source tools consistent with the principles of reproducible research and open science practices. This open-source nature will further make this project a catalyst for future methodological innovations and clinical investigations. Our long-term goal is to streamline and standardize atrial fibrosis quantification for the clinical management of AF patients, and this project will establish the groundwork for achieving this goal.