MR is well suited for quantifying fat depots (e.g., visceral, subcutaneous, hepatic, muscular) and for helping to determine the role of genetic, environmental, and therapeutic factors on lipid accumulation, metabolism, and disease states. Colleagues and I will advance technologies for characterizing fat depots in rodents and apply them to important mouse models of obesity, including future studies of a unique set of 22 chromosome substitution strains to enable efficient discovery of obesity-related genes. Imaging technology developments will significantly increase the speed and robustness of image acquisition and analysis. We will develop radial Multi-Point Dixon (MPD) acquisitions to obtain a high resolution whole-body assessment of lipid levels. Ratio images will be created which eliminate any effect of receive coil inhomogeneity and which greatly facilitate accurate, semi-automated image analysis. MR spectroscopy of skeletal muscle will assess both saturated and unsaturated myocellular lipid levels. Because of the size of the data sets, we will develop image analysis/visualization software, including interactive, 3D segmentation, and volume measurements using a voxel mixture model that accounts for partial volume/mixed voxels. Our goal is a comprehensive imaging and "electronic imaging report" in about 1.5 hours. Once technologies are firmly established, we will acquire baseline data on the two parent strains and dynamically characterize fat depots during weight gain/loss resulting from high and low fat diets. We will comprehensively evaluate the effects of diet and genetics on lipid accumulation and metabolism in the liver, muscle, and adipose tissue compartments and compare results to measures of insulin resistance so as to determine early biomarkers of metabolic disease. My intention is to create a paradigm for phenotyping mouse models of obesity and to dissect the role of genetics and perturbations such as dietary change, exercise, or drug therapy on fat depots.

Public Health Relevance

Obesity is associated with many serious medical conditions, including high blood pressure, diabetes, heart disease, stroke, etc. We will develop robust and rapid non-invasive imaging technologies to study mouse models of obesity and to dissect the role of genetics and perturbations such as dietary change, exercise, or drug therapy on fat depots. The MRI techniques developed here can be readily translated to studies of human metabolic disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
5F30DK082132-05
Application #
8291368
Study Section
Special Emphasis Panel (ZRG1-F15-V (20))
Program Officer
Castle, Arthur
Project Start
2008-07-01
Project End
2013-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
5
Fiscal Year
2012
Total Cost
$38,832
Indirect Cost
Name
Case Western Reserve University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106
Narayan, Sreenath; Flask, Chris A; Kalhan, Satish C et al. (2015) Hepatic fat during fasting and refeeding by MRI fat quantification. J Magn Reson Imaging 41:347-53
Narayan, Sreenath; Kalhan, Satish C; Wilson, David L (2013) Recovery of chemical estimates by field inhomogeneity neighborhood error detection (REFINED): fat/water separation at 7 tesla. J Magn Reson Imaging 37:1247-53
Miao, Jun; Huang, Feng; Narayan, Sreenath et al. (2013) A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: a preliminary study. Magn Reson Imaging 31:596-603
Miao, Jun; Guo, Weihong; Narayan, Sreenath et al. (2013) A simple application of compressed sensing to further accelerate partially parallel imaging. Magn Reson Imaging 31:75-85
Johnson, David H; Narayan, Sreenath; Wilson, David L et al. (2012) Body composition analysis of obesity and hepatic steatosis in mice by relaxation compensated fat fraction (RCFF) MRI. J Magn Reson Imaging 35:837-43
Miao, Jun; Wong, Wilbur C K; Narayan, Sreenath et al. (2011) Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging. Med Phys 38:4760-73
Narayan, Sreenath; Huang, Fangping; Johnson, David et al. (2011) Fast lipid and water levels by extraction with spatial smoothing (FLAWLESS): three-dimensional volume fat/water separation at 7 Tesla. J Magn Reson Imaging 33:1464-73
Huang, Fangping; Narayan, Sreenath; Wilson, David et al. (2011) A fast iterated conditional modes algorithm for water-fat decomposition in MRI. IEEE Trans Med Imaging 30:1480-92
Miao, Jun; Wong, Wilbur C K; Narayan, Sreenath et al. (2011) K-space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging. Med Phys 38:6138-42