MRI has enormous potential for dynamic imaging of various diseases and physiological processes, which has not been fully utilized for clinical applications due to the limited imaging speed of existing technology. The quest for higher imaging speeds has been a major driving force for MRI research since its invention. Although tremendous progress has been made in fast MRI technology over the last three decades, virtually all MRI applications could benefit from additional speedups, and many potential applications would become possible only with significant acceleration. The primary objective of the proposed project is to produce significantly faster MRI technology by leveraging the recent breakthroughs in sparse sampling theory and the novel work of the PI s group in this area. This objective will be achieved with specific research efforts on: a) developing and optimizing a novel method for image reconstruction from highly undersampled (k, t)-space data using both partial separability and spatial-spectral constraints, b) analyzing and characterizing the resolution and noise properties of the proposed methods, and c) evaluating and validating the proposed method for cardiac imaging applications using phantom and rat studies. The outcome of the research effort will be significant in several ways. First, it will provide a new mathematical and algorithmic framework that effectively exploits the sparsity and partial separability of multidimensional MRI signals;this framework will enable sparse data sampling and significantly accelerate current MRI methods. Second, it will produce new MR imaging technology that will enhance the performance of existing MRI systems and provide a new way to optimize the design of MR data acquisition and image reconstruction in current and next-generation MRI systems. Third, it will enable a range of challenging dynamic imaging experiments, including realtime 3D cardiac imaging applications (e.g., functional assessment of transplanted hearts).

Public Health Relevance

This project will generate novel technology for fast dynamic magnetic resonance imaging (MRI), which will significantly enhance the clinical utility of MRI for the detection and diagnosis of diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB013695-04
Application #
8719997
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2011-09-15
Project End
2015-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Illinois Urbana-Champaign
Department
Type
Organized Research Units
DUNS #
City
Champaign
State
IL
Country
United States
Zip Code
61820
Ma, Chao; Lam, Fan; Johnson, Curtis L et al. (2016) Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model. Magn Reson Med 75:488-97
Lam, Fan; Liu, Ding; Song, Zhuang et al. (2016) A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints. Magn Reson Med 75:433-40
Lam, Fan; Ma, Chao; Clifford, Bryan et al. (2016) High-resolution (1) H-MRSI of the brain using SPICE: Data acquisition and image reconstruction. Magn Reson Med 76:1059-70
Zhao, Bo; Lu, Wenmiao; Hitchens, T Kevin et al. (2015) Accelerated MR parameter mapping with low-rank and sparsity constraints. Magn Reson Med 74:489-98
Ma, Chao; Liang, Zhi-Pei (2015) Design of multidimensional Shinnar-Le Roux radiofrequency pulses. Magn Reson Med 73:633-45
Christodoulou, Anthony G; Wu, Yijen L; Hitchens, T Kevin et al. (2014) Self-navigated low-rank MRI for MPIO-labeled immune cell imaging of the heart. Conf Proc IEEE Eng Med Biol Soc 2014:1529-32
Zheng-Hua Wu; Fan Lam; Chao Ma et al. (2014) Improved image reconstruction for subspace-based spectroscopic imaging using non-quadratic regularization. Conf Proc IEEE Eng Med Biol Soc 2014:2432-5
Zhao, Bo; Lam, Fan; Liang, Zhi-Pei (2014) Model-based MR parameter mapping with sparsity constraints: parameter estimation and performance bounds. IEEE Trans Med Imaging 33:1832-44
Lam, Fan; Liang, Zhi-Pei (2014) A subspace approach to high-resolution spectroscopic imaging. Magn Reson Med 71:1349-57
Christodoulou, Anthony G; Hitchens, T Kevin; Wu, Yijen L et al. (2014) Improved subspace estimation for low-rank model-based accelerated cardiac imaging. IEEE Trans Biomed Eng 61:2451-7

Showing the most recent 10 out of 22 publications