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

基本信息

批准号：
10351919
负责人：
Suvai Gunasekaran
金额：
$ 9.87万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10351919
关键词：
3-Dimensional Address Adult Affect 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 Gold Grant Hand Health Heart Heart Diseases Hypertrophic Cardiomyopathy Image Image Enhancement Imaging Techniques Left ventricular structure Manuals 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 base 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

项目摘要

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、舒张性心脏病尽管心律失常、心肌炎和心源性猝死很普遍。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脏 MRI (CMR) 是诊断小儿心肌病的金标准无创筛查工具。通过细胞外体积 (ECV) 和晚期钆检测弥漫性和局灶性纤维化不幸的是，ECV 和 LGE CMR 分别需要增强 (LGE) 成像。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 也需要进行。受试者屏住呼吸以获得准确的成像然而，许多儿科患者无法做到这一点。充分屏住呼吸，因此接受全身麻醉 (GA)，这并不理想，因为此外，当前的 2D 会带来额外的健康风险和巨大的财务成本。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。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尽管它在评估心肌纤维化方面具有巨大的潜力。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，在本研究中，我寻求通过 (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。