Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

 描述(申请人提供)：肥胖症已经在成人和儿童中达到了全球流行的比例。肥胖对心血管疾病有重要影响，因为它对心脏功能、结构和各种心血管疾病危险因素有不利影响。磁共振成像在估计这些变化和对肥胖受试者重大晚期心脏事件的风险进行分层方面具有巨大的潜力。然而，肥胖和相关的肺部并发症导致的生理变化往往使许多肥胖受试者难以遵守当前的临床方案，这些方案需要多次屏气和较长的扫描时间。对于肥胖者的心脏评估迫切需要短时间自由呼吸方案。这项建议的主要目标是开发一种简短的3D自由呼吸和非门控心脏成像方案，以在大约20分钟的扫描时间内评估肥胖者的心脏结构、功能、灌注和纤维化。该协议是由新的非门控序列和新的流形正则化框架的协同发展而实现的。重建框架利用了数据集中的图像和补丁的流形结构，非常适合利用非门控3-D序列的灵活性和高采集效率。主要的假设是隐式运动补偿和运动分解重建方案能够很好地恢复协议中的数据集，从高采样率的数据中恢复。我们将定量确定自由呼吸和非门控框架的效用，以提供等同于当前屏气采集的重建。这一框架有望显著提高肥胖受试者的依从性。此外，这种方法还提供了具有不同对比度的联合配准的3-D体积，这将极大地提高量化、可视化和放射学解释。流形学习框架功能强大且极具创新性；它可以很容易地应用于心脏成像以外的各种动态应用(声道成像、肝脏成像、肺成像)。我们的团队完全有资格执行拟议的研究，因为除了广泛的初步数据外，我们在专业知识(包括信号处理、磁共振物理学和放射学)方面的综合范围和广度。

 DESCRIPTION (provided by applicant): Obesity has reached global epidemic proportions in both adults and children. Obesity has a major impact on cardiovascular (CV) disorders because of its adverse effects on cardiac function, structure, and various CV risk factors. MR imaging has great potential in estimating these changes and stratifying obese subjects for risk of major advanced cardiac events. However, the physiological changes resulting from obesity and associated pulmonary comorbidities often make it difficult for many obese subject to comply with current clinical protocols that require several breath holds and long scan time. Short free breathing protocols are urgently needed for the cardiac evaluation of obese subjects. The main goal of this proposal is to develop a short 3-D free-breathing & un-gated cardiac imaging protocol to evaluate cardiac structure, function, perfusion, and fibrosis in obese subjects in around twenty minutes of scan time. This protocol is enabled by synergistic developments in novel ungated sequences and a novel manifold regularization framework. The reconstruction framework, which exploits the manifold structure of images and patches in the dataset, is ideally suited to harness the flexibility and high acquisition efficiency of ungated 3-D sequences. The main hypothesis is that the implicit motion compensated and motion resolved reconstruction scheme will provide good recovery of the datasets in the protocol from highly under sampled data. We will quantitatively determine the utility of the free-breathing & ungated framework to provide reconstructions that are equivalent to current breath-hold acquisitions. This framework is expected to significantly improve the compliance of obese subjects. In addition, this approach also provides co-registered 3-D volumes with different contrasts, which will greatly improve quantification, visualization, and radiologic interpretation. The manifold learning framework is powerful and highly innovative; it can be readily applied to a variety of dynamic applications beyond cardiac imaging (vocal tract imaging, liver imaging, lung imaging). Our team is well qualified to perform the proposed research because of our combined scope and breadth in expertise (including signal processing, MR physics, and radiology), in addition to the extensive preliminary data.