Key result
The proposed bias field compensation method achieved a significantly higher reduction of mean coefficients of variance (83.6%) compared with the LEMS algorithm (54.3%) at higher bias field amplitudes.
The proposed method enables accurate segmentation of abdominal adipose tissue in MR images with severe inhomogeneities.
May improve abdominal adipose segmentation on inhomogeneous MR images; extends bias-correction methods but leaves clinical adoption open.
PURPOSE: To reliably compensate bias field effects in abdominal areas to accurately quantify visceral adipose tissue using standard T1-weighted sequences on MR scanners with up to 3 Tesla (T) field strength. MATERIALS AND METHODS: Compensation is achieved in two steps: The bias field is first estimated by picking and fitting sampling points from the subcutaneous adipose tissue, using active contours and a thin plate fitting spline. Then, additional sampling points from visceral adipose tissue compartments are detected by thresholding and the bias field estimation is refined. It was compared with an established method using a simulated abdominal image and real 3T data. RESULTS: At low bias field amplitudes (40-50%), the simulation study showed a good reduction of the mean coefficients of variance (CV) for both approaches (>80%). At higher amplitudes, the CV reduction was significantly higher for our approach (83.6%), compared with LEMS (54.3%). In the real data study, our approach showed reliable reduction of the inhomogeneities, while the LEMS algorithm sometimes even amplified the inhomogeneities. CONCLUSION: The proposed method enables accurate and reliable segmentation of abdominal adipose tissue using simple thresholding techniques, even in severely corrupted images slices, obtained when using high field strengths and/or phased-array coils.
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Würslin et al. (2011) studied Visceral adipose tissue quantification in abdominal MR images. Proposed bias field compensation method using active contours and thin plate fitting spline vs. LEMS algorithm was evaluated on Reduction of the mean coefficients of variance (CV) at higher bias field amplitudes. The proposed bias field compensation method achieved a significantly higher reduction of mean coefficients of variance (83.6%) compared with the LEMS algorithm (54.3%) at higher bias field amplitudes.
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