Fully automatic and nonparametric quantification of adipose tissue in fat-water separation MR imaging.

Despite increasing demand and research efforts, currently there is no consensus on the protocol for automated and reliable quantification of adipose tissue (AT) and visceral adipose tissue (VAT) using MRI. The purpose of this study was to propose a novel computational method with enhanced objectiveness for the quantification of AT and VAT in fat-water separation MRI. 3T data from IDEAL were acquired for the fat-water separation. Fat tissues were separated from nonfat regions (background air, bone, water, and other nonfat tissues) using K-means clustering (K = 2). From the binary fat mask, arm regions were separated from body based on the relative size of connected component. AT was obtained from the binary body fat mask. With the initial contour as the outer boundary of body fat, the subcutaneous adipose tissue (SAT) and VAT were separated using deformable model driven by a specifically generated deformation field pointing to the inner boundary of SAT. The proposed method was tested on 16 patients with dyslipidemia and evaluated by comparing the correlation with semi-automatic segmentation results. Good robustness was also observed in the proposed method from the Bland-Altman plots. Compared to other established fat segmentation methods, the proposed method is highly objective for fat-water separation MRI with minimal variability induced by subjective parameter settings.