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 #
5R01HL094557-05
Application #
8586534
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Evans, Frank
Project Start
2010-03-15
Project End
2014-11-30
Budget Start
2013-12-01
Budget End
2014-11-30
Support Year
5
Fiscal Year
2014
Total Cost
$417,923
Indirect Cost
$151,730
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, Alice C; Kretzler, Madison; Sudarski, Sonja et al. (2016) Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 51:349-64
Chen, Yong; Lee, Gregory R; Aandal, Gunhild et al. (2016) Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA. Magn Reson Med 75:1457-65
Wech, Tobias; Seiberlich, Nicole; Schindele, Andreas et al. (2016) Development of Real-Time Magnetic Resonance Imaging of Mouse Hearts at 9.4 Tesla--Simulations and First Application. IEEE Trans Med Imaging 35:912-20
Deshmane, Anagha; Blaimer, Martin; Breuer, Felix et al. (2016) Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding. Magn Reson Med 75:883-96
Hamilton, Jesse I; Jiang, Yun; Chen, Yong et al. (2016) MR fingerprinting for rapid quantification of myocardial T1 , T2 , and proton spin density. Magn Reson Med :
Cao, Zhipeng; Oh, Sukhoon; Otazo, Ricardo et al. (2015) Complex difference constrained compressed sensing reconstruction for accelerated PRF thermometry with application to MRI-induced RF heating. Magn Reson Med 73:1420-31
Chen, Yong; Lee, Gregory R; Wright, Katherine L et al. (2015) Free-breathing liver perfusion imaging using 3-dimensional through-time spiral generalized autocalibrating partially parallel acquisition acceleration. Invest Radiol 50:367-75
Wright, Katherine L; Lee, Gregory R; Ehses, Philipp et al. (2014) Three-dimensional through-time radial GRAPPA for renal MR angiography. J Magn Reson Imaging 40:864-74
Wright, Katherine L; Chen, Yong; Saybasili, Haris et al. (2014) Quantitative high-resolution renal perfusion imaging using 3-dimensional through-time radial generalized autocalibrating partially parallel acquisition. Invest Radiol 49:666-74
Aandal, Gunhild; Nadig, Vidya; Yeh, Victoria et al. (2014) Evaluation of left ventricular ejection fraction using through-time radial GRAPPA. J Cardiovasc Magn Reson 16:79

Showing the most recent 10 out of 23 publications