Automated method for accurate abdominal fat quantification on water-saturated magnetic resonance images

Purpose: To introduce and evaluate the performance of an automated fat quantification method for water-saturated magnetic resonance images. Materials and Methods: A fat distribution model is proposed for fat quantification on water saturated magnetic resonance images. Fat from both full- and partial-volume voxels are accounted for in this model based on image intensity histogram analysis. An automated threshold method is therefore proposed to accurately quantify total fat. This method is compared to a traditional full-volume-fat-only method in phantom and human studies. In the phantom study, fat quantification was performed on MR images obtained from a human abdomen oil phantom and was compared with the true oil volumes. In the human study, results of the two fat quantification methods of six subjects were compared on abdominal images with different spatial resolutions. Results: In the phantom study, the proposed method provided significantly more accurate estimations of true oil volumes compared to the reference method (P < 0.0001). In human studies, fat quantification using the proposed method gave much more consistent results on images with different spatial resolutions, and on regions with different degrees of partial volume averaging. Conclusion: The proposed automated method is simple, rapid, and accurate for fat quantification on water-saturated MR images.