The objective of this proposal is to produce a new level of gains in imaging speed and SNR for cardiac and vascular imaging by combining novel concepts of non-Cartesian parallel imaging techniques with the newly emerging compressed sampling theory. Compressed sensing promises to revolutionize the field of MRI by breaking the traditional link between imaging time and SNR. Here we will exploit these concepts to develop a set of completely new imaging strategies with dramatic increases in SNR and imaging speed. We specifically address computational limitations by developing an open source software distribution for high-end graphical processing units. These processors promise to dramatically reduce computational time across the board in medical imaging. Ultimately we believe that these technologies, when viewed as a whole, will result in a novel class of methods for cardiac and vascular diagnosis which will provide an increase in image quality, SNR and speed in MRI, perhaps unparalleled in the evolution of MRI, resulting in dramatically improved imaging of MR angiography, cardiac function and cardiac perfusion.
Our specific aims are to: 1) develop and evaluate improved methods to acquire, and reconstruct multislice non-Cartesian parallel imaging methods for 2D MRI applications 2) develop and evaluate robust combined non-Cartesian parallel imaging and compressed sensing methods 3) develop and evaluate improved computational methods based on graphical processing units (GPUs) for the calculation of non-Cartesian parallel imaging, CG-HYPR and combined methods to achieve clinically acceptable reconstruction times and 4) validate parallel CG-HYPR methods for the evaluation of cardiovascular disease as a means to shorten total exam time and increase image quality.

Public Health Relevance

The objective of this proposal is to produce a new level of gains in imaging speed and SNR for cardiac and vascular imaging by combining novel concepts of non-Cartesian parallel imaging techniques with the newly emerging compressed sampling theory. Compressed sensing promises to revolutionize the field of MRI by breaking the traditional link between imaging time and SNR. Here we will exploit these concepts to develop a set of completely new imaging strategies with dramatic increases in SNR and imaging speed. Unfortunately to date, the computation time in these methods has ranged up to several hours per image. We specifically address this limitation in this project through the use of the modern generation of graphical processing units (GPUs). These processors promise to dramatically reduce computational time across the board in medical imaging. Ultimately we believe that these technologies, when viewed as a whole, will result in a novel class of methods for cardiac and vascular diagnosis which will provide an increase in image quality, SNR and speed in MRI, perhaps unparalleled in the evolution of MRI, resulting in dramatically improved imaging of MR angiography, cardiac function and cardiac perfusion.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL094557-04
Application #
8392238
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Evans, Frank
Project Start
2010-03-15
Project End
2014-11-30
Budget Start
2012-12-01
Budget End
2013-11-30
Support Year
4
Fiscal Year
2013
Total Cost
$631,398
Indirect Cost
$158,419
Name
Case Western Reserve University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106
Yang, Mingrui; Ma, Dan; Jiang, Yun et al. (2018) Low rank approximation methods for MR fingerprinting with large scale dictionaries. Magn Reson Med 79:2392-2400
Chen, Yong; Lo, Wei-Ching; Hamilton, Jesse I et al. (2018) Single breath-hold 3D cardiac T1 mapping using through-time spiral GRAPPA. NMR Biomed 31:e3923
Ma, Dan; Jiang, Yun; Chen, Yong et al. (2018) Fast 3D magnetic resonance fingerprinting for a whole-brain coverage. Magn Reson Med 79:2190-2197
McGivney, Debra; Deshmane, Anagha; Jiang, Yun et al. (2018) Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting. Magn Reson Med 80:159-170
Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar et al. (2018) Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling. Magn Reson Med 79:933-942
Pahwa, Shivani; Liu, Hao; Chen, Yong et al. (2018) Quantitative perfusion imaging of neoplastic liver lesions: A multi-institution study. Sci Rep 8:4990
Wright, Katherine L; Jiang, Yun; Ma, Dan et al. (2018) Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling. Magn Reson Imaging 50:68-77
Coristine, Andrew J; Chaptinel, Jerome; Ginami, Giulia et al. (2018) Improved respiratory self-navigation for 3D radial acquisitions through the use of a pencil-beam 2D-T2 -prep for free-breathing, whole-heart coronary MRA. Magn Reson Med 79:1293-1303
Ye, Huihui; Cauley, Stephen F; Gagoski, Borjan et al. (2017) Simultaneous multislice magnetic resonance fingerprinting (SMS-MRF) with direct-spiral slice-GRAPPA (ds-SG) reconstruction. Magn Reson Med 77:1966-1974
Panda, Ananya; Mehta, Bhairav B; Coppo, Simone et al. (2017) Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng 3:56-66

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