The non-invasive acquisition of spatial distributions of biochemical information with magnetic resonance spectroscopic imaging (MRSI) promises to enormously enhance our ability to detect and diagnose disease, to monitor the efficacy of therapy, and to increase our understanding of the body's basic physiology. However, clinical and research applications of MRSI have been developing slowly due to several long-standing technical barriers, including long data acquisition time, poor spatial resolution, low signal-to-noise (SNR), and overwhelming nuisance signals (for 1H-MRSI). In order for MRSI to become a routine diagnostic and research tool, accurate, spatially- resolved spectral information must be obtained reproducibly in a time acceptable to patients. Current MRSI methods, after more than three decades of research efforts and progress, still fall short of providing an adequate combination of resolution, speed, and SNR for routine in vivo applications. The primary objective of the proposed project is to develop a new technology for MRSI to achieve an unprecedented combination of resolution, speed, and SNR for 3D ultra-high resolution MRSI of the brain. This objective will be achieved by leveraging the recent breakthroughs in sparse sampling theory and subspace imaging through concentrated research efforts on: a) developing and implementing a novel data acquisition method and pulse sequence for highly accelerated spatiospectral encodings with sparse sampling of (k, t)-space; b) developing and implementing effective methods for removal of nuisance signals (water and lipid signals) and for image reconstruction from highly sparse (k, t)-space 1H-MRSI data; and c) analyzing the resolution and noise properties and reproducibility of the proposed method by theoretical analysis, computer simulations, and experimental studies. The successful completion of the project will have a major impact on the field of MRSI by providing a key technology advance to make high-resolution MRSI practical. It will also open up new exciting opportunities for further MRSI technology development and innovations that will enable a wide range of basic and clinical applications of high-resolution MRSI.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB021013-02
Application #
9069850
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2015-07-01
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Illinois Urbana-Champaign
Department
Type
Organized Research Units
DUNS #
041544081
City
Champaign
State
IL
Country
United States
Zip Code
61820
Peng, Xi; Lam, Fan; Li, Yudu et al. (2018) Simultaneous QSM and metabolic imaging of the brain using SPICE. Magn Reson Med 79:13-21
Ma, Chao; Clifford, Bryan; Liu, Yuchi et al. (2017) High-resolution dynamic 31 P-MRSI using a low-rank tensor model. Magn Reson Med 78:419-428
Ma, Chao; Lam, Fan; Ning, Qiang et al. (2017) High-resolution 1 H-MRSI of the brain using short-TE SPICE. Magn Reson Med 77:467-479
Li, Yudu; Lam, Fan; Clifford, Bryan et al. (2017) A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging. IEEE Trans Biomed Eng 64:2486-2489
Ning, Qiang; Ma, Chao; Lam, Fan et al. (2017) Spectral Quantification for High-Resolution MR Spectroscopic Imaging With Spatiospectral Constraints. IEEE Trans Biomed Eng 64:1178-1186
He, Jingfei; Liu, Qiegen; Christodoulou, Anthony G et al. (2016) Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors. IEEE Trans Med Imaging 35:2119-29
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