Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI

Purpose: To demonstrate that unsupervised assessment of abdominal adipose tissue distribution by magnetic resonance imaging (MRI) can be improved by integrating automatic correction of signal inhornogeneities. Materials and Methods: Twenty subjects (body mass index [BMIl 23.7-44.0 kg/m2) underwent abdominal' (32 slices) MR imaging with a 1.9T Elscint Prestige scanner. Many images were affected by relevant-intensity distortions. Unsupervised segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) was' performed by a previously validated' algorithm' exploiting standard fuzzy clustering segmentation. Images were= also processed' by an improved versiori of the software, including automatic correction of intensity inhomogeneities. To'assess the effectiveness of the two methods SAT and' VAT volumes were compared with manual analysis performed by a trained operator. Results: Coefficient of variation between manual and unsupervised analysis was significantly improved by inhomogeneides correction in SAT evaluation. Systematic underestimation of SAT was also corrected. A less important performance improvement was found in VAT measurement. Conclusion: The results of this study suggest that the compensation of signal inhomogeneides greatly improves the effectiveness of the unsupervised assessmentof abdominatfat. ation and less significant;imVATmeasbrement.