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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB019961-01A1
Application #
9104637
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2016-04-01
Project End
2020-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Iowa
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52246
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
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
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
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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