Monte Carlo modeling enables characterization of MR signals in various tissues, and has been applied to liver MR in the presence of fat. However, Monte Carlo modeling requires accurate information about the underlying tissue properties. In this work, we investigate the size and clustering of fat droplets in the liver using stereology and spatial statistics for three human liver biopsy samples with steatosis. Results show that the generalized gamma distribution function can accurately determine the size and location distributions of fat droplets. This may enable analysis of the underlying biophysical mechanisms between fat fraction and R2* from microscopic magnetic sensitivity.
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