Quantification of absolute fat mass using an adipose tissue reference signal model.

Obesity is a growing epidemic that affects children and adults in the United States. Increased health risks include cardiovascular, endocrine, and metabolic diseases, which have been linked to fat accumulation in adipose tissue, skeletal muscles, and organs (ectopic fat) (1,2). Noninvasive assessment of fat distribution has become an important component in obesity research and preventive medicine, and accurate quantitative estimates of fat are sought as biomarkers for disease stratification. Several methods are available for estimating body fat content (3). Anthropometric indices, hydrodensitometry, and air displacement plethysmography estimate total percent body fat with predictive equations. Bioelectric impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) provide quantitative endpoints of total and regional body fat mass, and are the predominant methods used in obesity studies. However, BIA and DXA can not differentiate abdominal adipose tissue from ectopic fat. Fat quantification using computed tomography (4) and MRI (5) have recently gained popularity. Their three-dimensional (3D) image volumes and high spatial resolution can highlight fat distributions within the abdomen. MRI's flexible signal contrast can further identify fat infiltration in organs and muscles, and its lack of ionizing radiation is suitable for studying obesity in young cohorts. One common MRI method uses T1-weighting to distinguish adipose from lean tissue (5). Quantification involves identifying “fat” voxels with a binary signal intensity histogram threshold. After image segmentation of the subcutaneous and visceral adipose tissue compartments, summation of voxels yields a volume of adipose tissue as the quantitative endpoint. This approach estimates adipose tissue, which also contains water, carbohydrate, and proteins in addition to fat (triglycerides). An alternative using spectrally selective radiofrequency (RF) pulses to excite fat protons was recently described (6), and a similar histogram procedure was used to quantify fat volume instead of adipose tissue volume. Both the T1 and spectrally selective RF methods assign each reconstructed voxel as either “fat” or “nonfat,” or assumptions regarding partial volume are made (6). While quantitative errors due to partial volume are minimal for the well-delineated subcutaneous adipose tissue, they can be significant within the visceral compartment and in ectopic fat where patterns of heterogeneous fat distribution are probable (7). Consequently, neither method has been extended to quantify nonuniform and diffusely distributed ectopic fat. Chemical-shift MR, such as 1H spectroscopy (8), in- and opposed-phase imaging (9), and generalized multipoint fat-water separation methods such as Iterative Decomposition with Echo Asymmetry and Least squares estimation—IDEAL (10), have been used to quantify ectopic fat in the study of liver steatosis (11,12) and in muscles (13). A relative fat-water signal fraction is typically reported as the quantitative endpoint, in spectroscopy as the ratio of areas under the fat and water spectra, and in the imaging methods as a fraction of the fat and water signal intensities. Because it is computed as a voxel-by-voxel ratio, one benefit of the signal fraction is that receiver coil sensitivities and RF transmit nonuniformities are approximately cancelled. Two works have shown that considerations of imaging parameters and data reconstruction steps are needed to further reduce T1 relaxation and noise bias in computing the fat-water signal fraction (14,15). In livers where high levels of iron may be present, T2* must also be included in the IDEAL algorithm (16). A recent study demonstrated the IDEAL fat-water signal fraction as a promising biomarker for fatty liver diseases (17). In this work, we formulate an approach to compute absolute fat mass by combining IDEAL with a reference fat signal from pure adipose tissue. As we will demonstrate, our desired quantitative endpoint is the estimation of fat in absolute units of mass (e.g. grams). We selected IDEAL as the basis of our experiments due to the technique's robust performance in the presence of B0 off-resonance (10,18) and its ability to distinguish fat and water on a voxel-by-voxel basis. Experiments are performed in phantoms and ex vivo swine specimens to test feasibility. In the ex vivo study, MRI results are compared against gold-standard lipid assay.