Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

肺动脉高压是一种病死率较高的慢性疾病。几个肺 降低发病率和死亡率的血管扩张剂，特别是在更早开始使用的情况下，一直是 FDA批准。然而，确定将从这种疗法中受益的个人 目前需要使用多种方式对心脏和肺进行广泛的测试， 从而导致高昂的医疗成本和诊断延迟。这项提议旨在引入一位先生 成像成像协议，通过提供以下信息在单个成像会话中诊断PH 心脏和肺脏系统的评估。目前的MRI方法有几种 上述设置中的限制。此续订申请旨在克服这些缺陷 使用一种新的生成风暴(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研究进展。这个框架显著地 改进了用于心脏MRI的分析流形正则化框架(STORM)，开发 在上一个项目中。建议的成像方法将通过与 目前的屏气核磁共振和CT成像方案。量化指标的初步应用 还将确定预测PH值的指标。

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.