Obesity is a growing epidemic in the United States, affecting multiple health outcomes in children, adolescents, and adults. Increasing data suggest that the link between obesity and poor health outcome is related to buildup of fat in specific areas (e.g. visceral fat inside the abdominal cavity) and/or infiltration of fat into the liver, pancreas, and other organs. Quantification of visceral fat and organ fat in vivo is generally performed by magnetic resonance imaging (MRI). MRI is well suited for this purpose because it is inherently three-dimensional, provides a sensitive mechanism for separating water and fat, and involves no ionizing radiation, leading to indefinite repeatability with ultra-low risk. Although MRI is becoming more frequently used in obesity research, its ability to directly quantify fat mass has not yet been developed and validated. Current measurements are either relative (e.g. fat signal fraction) or indirect (e.g. adipose tissue volume). Moreover, current MRI protocols are limited by the high-cost of magnet time, and the long and/or multiple breath-holds that are uncomfortable and reduce accuracy. The objective of this proposal is to overcome these limitations and develop and fully validate a novel MRI-based method for the rapid quantification of fat mass throughout the abdomen. Specifically, we will (1) develop signal calibration procedures that allow for the quantification of fat mass on a voxel-by-voxel basis, and (2) evaluate the accuracy of this quantification in swine, by comparing measurements of fat mass in adipose tissue, whole organs, and muscle, with post-mortem chemical analysis (the current gold standard). The SIGNIFICANCE of this proposal lies in the development of a new, promising, and potentially cost-effective tool for assessing fat mass and organ fat infiltration. The APPROACH utilizes recent advances in rapid MRI fat-water separation, calibration scans, and rigorous validation in an animal model. The INNOVATION lies in the application of novel signal models and calibration schemes for quantifying fat mass from signal intensity. The INVESTIGATORS include two MR physicists with expertise in the development of rapid MRI techniques for cardiovascular disease assessment and an established obesity researcher with expertise in the validation of body composition techniques. The ENVIRONMENT at USC provides generous access to research- dedicated imaging facilities, animal research support, and infrastructure for this translational research.

Public Health Relevance

Abdominal obesity is a growing problem among Americans, and is linked with increased risks of fatty liver disease, heart disease, and type-2 diabetes, among other things. This proposal will develop a new non-invasive test based on magnetic resonance imaging that determines the mass and location of fat within the abdomen, including critical organs, and may potentially be a useful tool in obesity research.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-EMNR-D (02))
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Horlick, Mary
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University of Southern California
Engineering (All Types)
Schools of Engineering
Los Angeles
United States
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Joshi, Anand A; Hu, Houchun H; Leahy, Richard M et al. (2013) Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI. J Magn Reson Imaging 37:423-30
Sharma, Samir D; Hu, Houchun H; Nayak, Krishna S (2013) Accelerated T2*-compensated fat fraction quantification using a joint parallel imaging and compressed sensing framework. J Magn Reson Imaging 38:1267-75
Sharma, Samir D; Hu, Houchun H; Nayak, Krishna S (2012) Accelerated water-fat imaging using restricted subspace field map estimation and compressed sensing. Magn Reson Med 67:650-9
Hu, Houchun Harry; Chung, Sandra A; Nayak, Krishna S et al. (2011) Differential computed tomographic attenuation of metabolically active and inactive adipose tissues: preliminary findings. J Comput Assist Tomogr 35:65-71
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Hu, H H; Nayak, K S; Goran, M I (2011) Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging. Obes Rev 12:e504-15
Hu, Houchun H; Li, Yan; Nagy, Tim R et al. (2011) Quantification of Absolute Fat Mass by Magnetic Resonance Imaging: a Validation Study against Chemical Analysis. Int J Body Compos Res 9:111-122
Hu, Houchun H; Nayak, Krishna S (2010) Change in the proton T(1) of fat and water in mixture. Magn Reson Med 63:494-501
Hu, Houchun H; Smith Jr, Daniel L; Nayak, Krishna S et al. (2010) Identification of brown adipose tissue in mice with fat-water IDEAL-MRI. J Magn Reson Imaging 31:1195-202
Hu, Houchun H; Kim, Hee-Won; Nayak, Krishna S et al. (2010) Comparison of fat-water MRI and single-voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans. Obesity (Silver Spring) 18:841-7

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