Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

自由呼吸的新颖计算框架

基本信息

批准号：
10583878
负责人：
Mathews Jacob
金额：
$ 55.06万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10583878
关键词：
3-Dimensional Acceleration Blood Preservation Breathing Cardiac Cardiac Catheterization Procedures Cardiopulmonary Cause of Death Chronic Disease Clinical Compensation Data Data Set Development Diagnosis Early Diagnosis Echocardiography Evaluation FDA approved Failure Functional Imaging HIV Health Care Costs Heart Image Individual Interstitial Lung Diseases Joints Learning Lung Lung diseases Magnetic Resonance Imaging Maps Measurement Measures Medical Care Costs Methods Modality Modeling Morbidity - disease rate Morphologic artifacts Motion Myocardium Paper Pattern Perfusion Population Protocols documentation Publications Pulmonary Hypertension Pulmonary function tests Radial Recovery Reproducibility Research Resolution Right ventricular strain Right ventricular structure Risk Scanning Scheme Scleroderma Site Slice Stress Structure System Testing Thick Thinness Time Treatment Efficacy Validation Vasodilator Agents Vendor World Health Organization X-Ray Computed Tomography blood pressure elevation cardiac magnetic resonance imaging computer framework constriction contrast enhanced cost data space heart function heart imaging high risk imaging modality improved journal article lung imaging mortality novel prospective pulmonary function right ventricular failure symposium unsupervised learning ventilation

项目摘要

Pulmonary hypertension (PH) is a chronic disease with high mortality. Several pulmonary vasodilators that reduce morbidity and mortality, especially with earlier initiation, have been FDA approved. However, the identification of individuals that would benefit from such therapies currently requires extensive testing of both the heart and the lungs using multiple modalities, resulting in high healthcare costs and delay in diagnosis. This proposal seeks to introduce an MR imaging imaging protocol to diagnose PH within a single imaging session by providing assessments of both cardiac and pulmonary systems. Current MRI methods have several limitations in the above setting. This renewal application aims to overcome these drawbacks using a novel generative SToRM (g-SToRM) framework, which capitalizes on the recent advances in deep generative models and unsupervised learning. This framework significantly improves the analysis manifold regularization framework (SToRM) for cardiac MRI, developed in the previous project. The proposed imaging methods will be validated by comparisons against current breath-held MRI and CT imaging protocols. The preliminary utility of the quantitative metrics to predict PH will also be determined.

肺动脉高压（PH）是一种死亡率很高的慢性疾病。几个肺血管扩张剂可降低发病率和死亡率，尤其是早期开始使用，已被 FDA 批准。然而，确定可以从此类疗法中受益的个体目前需要使用多种方式对心脏和肺部进行广泛的测试，导致高昂的医疗费用和诊断延误。该提案旨在引入 MR 成像协议通过提供在单个成像会话中诊断 PH 心脏和肺系统的评估。目前的MRI方法有以下几种上述设置的局限性。此续订申请旨在克服这些缺点使用新颖的生成 SToRM (g-SToRM) 框架，该框架利用了最近的深度生成模型和无监督学习的进展。这个框架显着改进了心脏 MRI 的分析流形正则化框架 (SToRM)，开发在之前的项目中。所提出的成像方法将通过与当前的屏气 MRI 和 CT 成像协议。量化的初步效用预测 PH 的指标也将被确定。