Non-contrast 3D T1p Mapping for Myocardial Fibrosis Quantification of Pediatric Cardiomyopathy Patients

用于小儿心肌病患者心肌纤维化定量的非对比 3D T1p 映射

• 批准号: 10579868
• 负责人: Suvai Gunasekaran
• 金额: $ 9.59万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579868
• 关键词: 3-Dimensional; Acceleration; Address; Adult; Affect; Agreement; Architecture; Arrhythmia; Brain; Breathing; Cardiac; Cardiomyopathies; Child; Childhood; Clinical; Clinical Medicine; Contrast Media; Data; Data Analyses; Development; Diffuse; Disease; Fibrosis; Financial cost; Functional disorder; Gadolinium; General Anesthesia; Grant; Hand; Health; Heart; Heart Diseases; Hypertrophic Cardiomyopathy; Image; Image Enhancement; Imaging Techniques; Left ventricular structure; Manuals; Maps; Marfan Syndrome; Measurement; Measures; Myocarditis; Nature; Patient Care; Patients; Pediatric Cardiomyopathy; Physiologic pulse; Population; Preparation; Renal function; Research; Resolution; Right ventricular structure; Risk; Sampling; Scanning; Screening procedure; Sex Differences; Speed; TNFSF15 gene; Tachycardia; Techniques; Technology; Testing; Thick; Time; Training; Training Activity; Work; age difference; allograft rejection; automated image analysis; automated segmentation; cardiac magnetic resonance imaging; career; clinical translation; congenital heart disorder; coronary fibrosis; deep learning; extracellular; heart allograft; image reconstruction; innovation; older patient; patient population; pediatric patients; prototype; racial difference; radio frequency; reconstruction; research clinical testing; study population; sudden cardiac death; young adult

PROJECT SUMMARY The development of myocardial fibrosis is associated with nearly all forms of pediatric heart disease including hypertrophic cardiomyopathy, congenital heart disease, diastolic dysfunction, arrhythmia, myocarditis, and sudden cardiac death. Despite the pervasive nature of myocardial fibrosis, the current technology available to detect fibrosis is suboptimal for studying pediatric cardiomyopathy. Cardiac MRI (CMR) is the gold standard noninvasive screening tool to detect both diffuse and focal fibrosis, through extracellular volume (ECV) and late gadolinium enhancement (LGE) imaging, respectively. Unfortunately, both ECV and LGE CMR require the administration of a gadolinium-based contrast agent (GBCA), which accumulates in the brain even when renal function is normal, including in children. In addition, traditional CMR requires subjects to hold their breath for accurate imaging. However, many pediatric patients cannot adequately hold their breath and so are put under general anesthesia (GA), which is not ideal as GA poses an additional health risk and significant financial cost. Furthermore, the current 2D techniques for fibrosis imaging have insufficient spatial resolution, and thus are only able to acquire data in sections of the left ventricle (6-10 mm thick) of the heart, completely missing fibrosis information in the right ventricle (3-5 mm thick), which is known to be the substrate for some tachycardia arrhythmias. Therefore, breathing, T1ρ mapping is a promising non-contrast CMR technique that can be used to detect both focal and diffuse myocardial fibrosis. Despite its enormous potential for assessment of myocardial fibrosis in pediatric patients, cardiac T1ρ mapping suffers from several technical limitations: (a) poor spatial resolution, (b) long scan time (up to 18 min), and (c) undeveloped pipeline for clinical integration. Additionally, the volumetric cardiac T1ρ mapping sequences that have been developed have only been tested on adult patients, and in very few subjects (n < 15). Therefore, in this study, I seek to address these limitations of 3D cardiac T1ρ mapping by (1) using innovative k-space sampling with deep learning for achieving unprecedented image quality with acceptable scan and reconstruction time, (2) implementing deep learning to automate image analysis and fibrosis quantification to make the information readily accessible for patient care, and (3) scanning a large population of pediatric patients to make this the most comprehensive T1ρ mapping study to date. there is a strong need to develop a non-contrast, free- volumetric imaging test for detecting fibrosis in pediatric patients.

项目概要 心肌纤维化的发展与几乎所有形式的儿科疾病有关 心脏病，包括肥厚性心肌病、先天性心脏病、舒张性心脏病 功能障碍、心律失常、心肌炎和心源性猝死。尽管具有普遍性 心肌纤维化，目前可用于检测纤维化的技术对于研究而言并不理想 小儿心肌病。心脏 MRI (CMR) 是黄金标准的无创筛查工具 通过细胞外体积 (ECV) 和晚期钆检测弥漫性和局灶性纤维化 分别是增强（LGE）成像。不幸的是，ECV 和 LGE CMR 都需要 施用钆基造影剂（GBCA），该造影剂会在大脑中积聚 即使肾功能正常，包括儿童。此外，传统的 CMR 需要 受试者屏住呼吸以获得准确的成像。然而，许多儿科患者无法 充分屏住呼吸，因此接受全身麻醉 (GA)，这并不理想，因为 GA 会带来额外的健康风险和巨大的财务成本。此外，当前的 2D 纤维化成像技术的空间分辨率不足，因此只能 获取心脏左心室（6-10 毫米厚）部分的数据，完全缺失 右心室（3-5 毫米厚）的纤维化信息，已知这是右心室纤维化的基础 一些心动过速心律失常。所以， 呼吸，T1ρ 映射是 有前景的非对比 CMR 技术，可用于检测局灶性和弥漫性 心肌纤维化。尽管其在评估心肌纤维化方面具有巨大潜力 对于儿科患者，心脏 T1ρ 映射存在一些技术限制：(a) 空间较差 分辨率，(b) 扫描时间长（长达 18 分钟），以及 (c) 尚未开发的临床整合流程。 此外，已开发的体积心脏 T1ρ 映射序列仅具有 已在成年患者和极少数受试者（n < 15）中进行了测试。因此，在这项研究中，我试图 通过 (1) 使用创新的 k 空间采样解决 3D 心脏 T1ρ 映射的这些局限性 通过深度学习实现前所未有的图像质量和可接受的扫描和 重建时间，（2）实施深度学习以自动化图像分析和纤维化 量化，以便患者护理能够轻松获取信息，以及 (3) 扫描大图 儿科患者群体使这项研究成为迄今为止最全面的 T1ρ 映射研究。 强烈需要开发一种非对比的、自由的 用于检测儿科患者纤维化的体积成像测试。