Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

University Of Iowa, Iowa City IA

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Abstract

? 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.