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.
The proposed project addresses the development of a novel algorithms and pulse sequences for free breathing & ungated dynamic MRI. This research has relevance to public health since this scheme can significantly improve patient comfort and compliance in cardiac scans.
| Ongie, Greg; Biswas, Sampurna; Jacob, Mathews (2018) Convex recovery of continuous domain piecewise constant images from nonuniform Fourier samples. IEEE Trans Signal Process 66:236-250 |
| Cui, Chen; Shah, Abhay; Wu, Xiaodong et al. (2018) A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE). Magn Reson Med 79:2401-2407 |
| Balachandrasekaran, Arvind; Magnotta, Vincent; Jacob, Mathews (2017) Recovery of Damped Exponentials Using Structured Low Rank Matrix Completion. IEEE Trans Med Imaging 36:2087-2098 |
| Biswas, Sampurna; Dasgupta, Soura; Mudumbai, Raghuraman et al. (2017) Subspace aware recovery of low rank and jointly sparse signals. IEEE Trans Comput Imaging 3:22-35 |
| Ongie, Greg; Jacob, Mathews (2017) A Fast Algorithm for Convolutional Structured Low-rank Matrix Recovery. IEEE Trans Comput Imaging 3:535-550 |
| Bhattacharya, Ipshita; Humston, Jonathan J; Cheatum, Christopher M et al. (2017) Accelerating two-dimensional infrared spectroscopy while preserving lineshapes using GIRAF. Opt Lett 42:4573-4576 |
| Bhattacharya, Ipshita; Jacob, Mathews (2017) Compartmentalized low-rank recovery for high-resolution lipid unsuppressed MRSI. Magn Reson Med 78:1267-1280 |
| Mohsin, Yasir Q; Lingala, Sajan Goud; DiBella, Edward et al. (2017) Accelerated dynamic MRI using patch regularization for implicit motion compensation. Magn Reson Med 77:1238-1248 |
| Mani, Merry; Jacob, Mathews; Kelley, Douglas et al. (2017) Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS). Magn Reson Med 78:494-507 |
| Ongie, Greg; Jacob, Mathews (2016) Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples. SIAM J Imaging Sci 9:1004-1041 |
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