This proposal seeks to improve the accuracy and repeatability of the characterization of ischemic heart disease using MRI. The first two aims focus on dynamic MRI for perfusion: (1) To develop radial k-space acquisition methods to provide rapid measurements of gadolinium concentration to improve the estimation of absolute myocardial perfusion. (2) To improve and automate post-processing analysis of the dynamic data to obtain robust quantitative perfusion estimates. New methods for registration, segmentation, and kinetic modeling will be developed to automatically produce maps of absolute perfusion along with a confidence map. (3) To determine the accuracy and repeatability of the new perfusion methods using 3 Tesla MRI. The repeatability of new methods for strain and scar mapping will also be assessed. (4) To apply the methods to longitudinal studies of patients undergoing bone marrow stem cell therapy as an adjunct to coronary artery bypass grafting. Methods: Development of radial perfusion sequences to provide accurate gadolinium concentrations from high dose dynamic MRI studies will be pursued using realistic computer simulations, phantoms, and human studies. Slice tracking acquisitions and combined registration/segmentation methods will be developed for robust automated processing of the data. Different physiological models will be developed and compared in their ability to provide absolute perfusion values. Mathematical methods for improving input functions by jointly estimating tissue and arterial input function model fits will be developed and tested. The new radial slice-tracking acquisition and processing methods will be validated by comparison in the same patients to quantitative perfusion from dynamic PET, along with comparison to a Cartesian acquisition. The repeatability of the perfusion methods and the strain and scar mapping techniques will be characterized in 20 subjects, each imaged twice. The new methods will then be applied to generate new insights into the myocardial response to stem cell therapy. The outcome of this project will be validated imaging protocols and software for use with cardiac MRI studies, improved methods for longitudinal assessment of myocardial perfusion, strain, and scar in vivo, and new information regarding the effect of stem cells on cardiac perfusion, strain, and scar. Such methods will be invaluable for evaluating novel therapies and for the detection and characterization of ischemic heart disease. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000177-06
Application #
7460546
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2002-07-01
Project End
2011-06-30
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
6
Fiscal Year
2008
Total Cost
$331,853
Indirect Cost
Name
University of Utah
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
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Pack, Nathan A; DiBella, Edward V R (2010) Comparison of myocardial perfusion estimates from dynamic contrast-enhanced magnetic resonance imaging with four quantitative analysis methods. Magn Reson Med 64:125-37
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Schabel, Matthias C; Fluckiger, Jacob U; DiBella, Edward V R (2010) A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations. Phys Med Biol 55:4783-806
Fluckiger, Jacob U; Schabel, Matthias C; Dibella, Edward V R (2009) Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI. Magn Reson Med 62:1477-86
Puchalski, Michael D; Williams, Richard V; Askovich, Bojana et al. (2009) Late gadolinium enhancement: precursor to cardiomyopathy in Duchenne muscular dystrophy? Int J Cardiovasc Imaging 25:57-63
Pack, Nathan A; DiBella, Edward V R; Rust, Thomas C et al. (2008) Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method. J Cardiovasc Magn Reson 10:52

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