Improved Imaging of Fibrosis in Atrial Fibrillation

改善心房颤动纤维化的成像

• 批准号: 10576920
• 负责人: EDWARD VR DIBELLA
• 金额: $ 74.78万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576920
• 关键词: Ablation; Adult; Affect; Age; Atrial Fibrillation; Biopsy; Blood; Cardiac; Cardiology; Cardiovascular Diseases; Cardiovascular system; Caring; Cicatrix; Clinical; Clinical Management; Clinical Sciences; Collagen; Compensation; Computing Methodologies; Data; Data Set; Detection; Development; Diffuse; Disease; Economics; Emergency department visit; Europe; Fibrosis; Future; Gadolinium; Goals; Health Care Costs; Health system; Heart; Heart Atrium; Histology; Hospitalization; Human; Hypertension; Image; Image Analysis; Image Enhancement; Imaging Device; Imaging Techniques; Individual; Injury; Intervention; Investigation; Left; Left atrial structure; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Medical; Methodology; Methods; Modeling; Motion; Myocardium; Nature; Office Visits; Patients; Persons; Play; Population; Prevalence; Problem Solving; Process; Protocols documentation; Reproducibility; Research; Research Personnel; Resolution; Respiration; Scanning; Sinus; Specialist; Standardization; Structure; Supervision; Techniques; Technology; Testing; Thinness; Time; Tissues; Trichrome stain; United States; Visualization; Work; automated segmentation; biomedical imaging; canine model; catalyst; clinical investigation; computer framework; coronary fibrosis; deep learning; experience; heart imaging; heart motion; heart rhythm; high resolution imaging; image reconstruction; imaging modality; improved; in vivo; innovation; machine learning method; mortality; non-invasive imaging; novel; open data; open source; open source tool; reconstruction; respiratory; stroke risk; tool; usability

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.

项目摘要 在美国，房颤是成年人第二常见的心血管疾病，仅次于 高血压，影响全球3300万人，美国和美国有700多万患者 欧洲，每年超过40万美国医院入院，每年500万次办公室就诊，27.6万次紧急情况 每年的诊室次数，美国每年增加260亿美元的医疗费用，以及五倍和几乎两倍的- 中风和死亡的风险分别增加了两倍。疾病过程中的一个关键标志是 左心房(LA)的胶原和纤维化形成。已经开发了磁共振成像方法来评估 左房包括纤维化，但仍不可靠，且有争议。具体地说，晚期Gd 增强磁共振成像(LGE)作为一种非侵入性成像工具，在活体可视化和 心房纤维化的量化。然而，不一致的图像质量和大量的专家- 纤维化量化过程中涉及的水平监督是其重复性的主要障碍。这 Proposal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 并确定这些测量的可重复性。目标1将引入稳健和 在LGE固定时间内获得各向同性分辨率的可重复的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整合到重建中 LGE数据的处理方法。Aim 2将提供精确的基于机器学习的方法来分割左侧 房壁和评价心房心肌纤维化。目标3将严格测试并进一步细化非 实验性纤维化犬模型的侵入性纤维化评估方法。目标4将量化 与现有方法相比，新的获取、重建和后处理方法的可重复性 方法在住院患者中进行。这项建议将直接影响临床科学和心脏病的护理标准 通过显著改善左房纤维化的LGE成像进行房颤治疗。工艺性 这项工作的贡献将进一步影响生物医学图像分析领域 基于核磁共振成像的心脏组织结构成像技术。为了提高透明度和再现性， 建议的工作将作为符合可重复研究原则的开源工具发布 和开放的科学实践。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规范 房颤患者心房纤维化的量化治疗，本项目将为房颤患者的临床治疗建立 为实现这一目标奠定基础。