11 March 2008 Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging
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We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed an MR ratio imaging technique that approximately yields lipid/{lipid + water}. In this work, we develop a statistical model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of variation of the denominator distribution (c.v.: 0-0:20) and correlation coefficient among the numerator and denominator (&rgr; 0-0.95), which cover the typical values that we found in MRI data sets (c.v.: 0:027-0:063, &rgr;: 0:50-0:75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs. +43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 ± 45mL, values ≈ 21% smaller than those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.
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Wilbur C. K. Wong, Wilbur C. K. Wong, David H. Johnson, David H. Johnson, David L. Wilson, David L. Wilson, "Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140S (11 March 2008); doi: 10.1117/12.772856; https://doi.org/10.1117/12.772856

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