Myocardial first-pass perfusion and late gadolinium enhancement (LGE) schemes are key components of most clinical cardiac MRI exams. The limitations of current MRI schemes often makes it challenging to simultaneously achieve high spatio-temporal resolution, sufficient spatial coverage, and good image quality in first-pass perfusion MRI, making it difficult to interpreting the results. Similarly, the large number of breath-holds and their long duration often makes LGE acquisitions challenging for many patients, resulting in significant motion artifacts and reduced patient throughput. In this context, there is an immediate clinical need for a novel dynamic imaging framework that can enable free-breathing acquisitions and considerably improve spatio-temporal resolution and coverage, without degrading the quality. The main objective of this proposal is to develop a novel dynamic imaging framework, which can enable free-breathing cardiac MRI and significantly accelerate it with minimal artifacts. We recently introduced a novel regularized reconstruction algorithm to significantly accelerate free-breathing dynamic MRI data. Preliminary validations of the algorithm demonstrated the ability of the proposed scheme to provide accelerations of up-to eleven fold with minor artifacts. The main focus of this proposal is to further improve the k-t SLR scheme and use it to realize high-resolution clinical myocardial perfusion and free-breathing LGE MRI. The successful completion of the proposed research will provide quantitative perfusion estimates with a temporal resolution of one heartbeat and spatial resolution of 0.15x0.15x0.8 cc from the entire heart, which is a four-fold improvement over current schemes. Similarly, we expect to considerably improve the patient compliance by relaxing the breath-holding requirement and reducing the scan time in LGE MRI data. These developments are quite significant and will considerably advance the state of the art in contrast-enhanced CMRI. The proposed algorithm is a radical departure from the classical approaches that rely on x-f space sparsity. In addition, we introduce non-convex spectral priors and additionally exploit the sparsity of the dynamic images to further improve the data fidelity and acceleration rate. Thus, the proposed scheme is highly innovative and its impact is expected to extend beyond the specific applications. Our team is well qualified to perform the proposed research because of our combined scope and breadth in expertise (including signal/image processing, MR physics, radiology, and cardiology), in addition to the extensive preliminary data.

Public Health Relevance

The proposed project addresses the development of a novel acquisition and data-processing scheme to improve the performance of contrast enhance cardiac MRI. This research has relevance to public health since this scheme can significantly improve the interpretation of the data and improve patient compliance and comfort. In addition, a reduction in scan time will improve throughput. Thus, the findings are ultimately expected to be applicable to improve the health of human beings.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BMIT-J (01))
Program Officer
Danthi, Narasimhan
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Iowa
Engineering (All Types)
Schools of Engineering
Iowa City
United States
Zip Code
Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P et al. (2016) Accelerated whole-brain multi-parameter mapping using blind compressed sensing. Magn Reson Med 75:1175-86
Lingala, Sajan Goud; DiBella, Edward; Jacob, Mathews (2015) Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI. IEEE Trans Med Imaging 34:72-85
Cui, Chen; Wu, Xiaodong; Newell, John D et al. (2015) Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm. Magn Reson Med 73:1289-99
Mohsin, Yasir Q; Ongie, Gregory; Jacob, Mathews (2015) Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image Recovery. IEEE Trans Med Imaging 34:2417-28
Mani, Merry; Jacob, Mathews; Guidon, Arnaud et al. (2015) Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data. Magn Reson Med 73:126-38
Mani, Merry; Jacob, Mathews; Magnotta, Vincent et al. (2015) Fast iterative algorithm for the reconstruction of multishot non-cartesian diffusion data. Magn Reson Med 74:1086-94
Mohsin, Yasir Q; Ongie, Gregory; Jacob, Mathews (2014) Accelerated MRI using iterative non-local shrinkage. Conf Proc IEEE Eng Med Biol Soc 2014:1545-8
Bhave, Sampada; Eslami, Ramin; Jacob, Mathews (2014) Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging. Magn Reson Med 71:469-76
Bhave, Sampada; Lingala, Sajan Goud; Jacob, Mathews (2014) A variable splitting based algorithm for fast multi-coil blind compressed sensing MRI reconstruction. Conf Proc IEEE Eng Med Biol Soc 2014:2400-3
Hu, Yue; Ongie, Greg; Ramani, Sathish et al. (2014) Generalized higher degree total variation (HDTV) regularization. IEEE Trans Image Process 23:2423-35

Showing the most recent 10 out of 18 publications