Improved Imaging of Fibrosis in Atrial Fibrillation

改善心房颤动纤维化的成像

• 批准号: 10392232
• 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/10392232
• 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; Computing Methodologies; Data; Data Set; Detection; Development; Diffuse; Disease; Economics; Emergency department visit; Europe; Fibrosis; Financial compensation; 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; Testing; Thinness; Time; Tissues; Trichrome stain; United States; Visualization; Work; base; 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

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，心房颤动（AF）是成年人中第二常见的心血管疾病，仅次于 高血压，影响全球3,300万人，美国有超过700万患者 欧洲，每年超过40万美国医院入院，每年500万个办公室访问，276,000紧急 每年的室访，美国医疗保健费用每年增加260亿美元，五倍，几乎两倍 折叠的中风和死亡率的风险增加。疾病过程中的关键标记是 左心房（LA）中的胶原蛋白和纤维化形成。已经开发了MR成像方法来评估 左心房，包括纤维化，但仍然不可靠且有争议。具体而言，晚期gadolinium 增强（LGE）MRI作为用于体内可视化和的非侵入性成像工具的关键作用 心房纤维化的定量。但是，不一致的图像质量和大量的专家 纤维化定量过程中涉及的水平监督是其可重复性的主要障碍。这 建议提供了一种新的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值的可重复性。 AIM 1将引入强大的 可重复的MRI采集和重建方法，用于在LGE的固定时间内获得各向同性分辨率 左心房的图像。独特的运动补偿模型将集成到重建中 LGE数据的方法。 AIM 2将提供基于机器学习的精确方法来细分左 心房壁并评估心肌纤维化。 AIM 3将严格测试并进一步完善非 - 具有纤维化的实验犬模型中的侵入性纤维化评估方法。 AIM 4将量化 与现有的新收购，重建和后处理方法的可重复性 患者的方法。该建议将直接影响心脏病学和心脏病学标准和 通过对左心房纤维化的LGE成像的显着改进，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 基于MRI的基于成像心脏组织结构的技术。为了促进透明度和可重复性， 拟议的努力将作为符合可再现研究原则一致的开源工具发布 和开放科学实践。这种开源性质将进一步使该项目成为未来的催化剂 方法论创新和临床研究。我们的长期目标是简化和标准化 对AF患者的临床管理的心房纤维化定量，该项目将确定 实现这一目标的基础。