Gadolinium Free Cardiac MR Imaging of Scar and Fibrosis

心血管疾病仍然是美国发病率和死亡率的主要原因。 States.疤痕或纤维化心脏组织的磁共振成像（MRI）是主要的诊断方法， 在冠心病或非缺血性心肌病患者中的预后作用 （NICM）。心脏MRI是一种非侵入性、多方面的成像方式，是临床的金标准 用于使用钆基造影剂注射的瘢痕和纤维化心脏组织成像。然而，在这方面， 这种钆基造影剂（GBCA）的施用缩短了扫描时间，增加了 扫描费用，并且在肾功能受损的患者中禁用？一种非常普遍 CHD患者的合并症。直到最近，GBCA被认为是安全的患者正常 肾功能;然而，有关于体内长期GBCA保留的新数据。我们的目标 开发两种互补的方法来减少GBCA在心肌或心肌瘢痕中的使用， 显像最初，我们将开发一个定量风险-获益模型，以识别低风险的NICM患者。 有可能出现心肌瘢痕。同时，我们将开发一种无GBCA的心脏MR 基于AI（MyoProbe.ai）的心肌组织探针平台，用于量化心脏的瘢痕区域。 为了实现这一点，我们将开发和评估一个个性化的，患者特异性的疤痕预测 通过训练模型学习， 基于非对比图像识别患者是否可能具有疤痕。如果不太可能 患者有疤痕，可以避免使用造影剂。开发和评估MyoProbe.ai 对于CHD患者心肌瘢痕的无GBCA量化，我们将使用AI整合信号 从MRI图像中获取强度和心脏运动数据，以准确定位和量化疤痕组织。这 然后，心脏病专家可以使用这些信息来诊断和治疗患者。我们将严格 使用回顾性和前瞻性方法验证我们的风险受益模型和AI心肌探头平台， 从多个医疗中心、MRI供应商和磁场收集心脏MRI图像 长处.我们的数据集将包括不同类型的NICM和CHD患者人群，以确保 我们的工作适用于许多不同类型的NICM和CHD患者。

Cardiovascular disease continues to be the leading cause of morbidity and mortality in the United States. Magnetic resonance imaging (MRI) of scarred or fibrotic heart tissue plays a major diagnostic and prognostic role in patients with coronary heart disease (CHD) or non-ischemic cardiomyopathy (NICM). Cardiac MRI is a non-invasive, multifaceted imaging modality and is the clinical gold standard for scar and fibrotic cardiac tissue imaging with use of gadolinium-based contrast injection. However, administration of such gadolinium-based contrast agents (GBCA) prolongs the scan time, increases scan cost, and is contra-indicated in patients with impaired kidney function? a highly prevalent comorbidity in CHD patients. Until recently, GBCA was presumed to be safe in patients with normal kidney function; however, there are emerging data on long-term GBCA retention in the body. We aim to develop two complimentary approaches to reduce GBCA use in myocardial, or cardiac muscle, scar imaging. Initially, we will develop a quantitative risk-benefit model to identify NICM patients with a low chance of having scarred myocardium. Concurrently, we will develop a GBCA-free cardiac MR myocardial tissue probe platform based on AI (MyoProbe.ai) to quantify scarred regions of the heart. To accomplish this, we will develop and evaluate an individualized, patient-specific scar prediction model to reduce GBCA use in NICM patients with different etiologies by training the model to learn to identify whether the patient is likely to have scarring based on non-contrast images. If it is unlikely that a patient has scarring, contrast administration can be avoided. To develop and evaluate MyoProbe.ai for GBCA-free quantification of myocardial scar in CHD patients, we will use AI to integrate signal intensity and heart motion data from MRI images to accurately locate and quantify scar tissue. This information can then be used by cardiologists to diagnose and treat the patient. We will rigorously validate our risk-benefit model and AI myocardial probe platform using retrospectively and prospectively collected cardiac MRI images from multiple healthcare centers, MRI vendors, and magnetic field strengths. Our dataset will include different types of NICM and CHD patient populations to ensure that our work is applicable to patients with many different types of NICM and CHD.