Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany.
J Magn Reson Imaging. 2011 Sep;34(3):716-26. doi: 10.1002/jmri.22682. Epub 2011 Jul 18.
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.
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.
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.
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.
利用最高可达 3 特斯拉（T）场强的磁共振扫描仪上的标准 T1 加权序列，可靠地补偿腹部区域的偏置场效应，以准确量化内脏脂肪组织。
补偿分两步进行：首先通过主动轮廓和薄板拟合样条从皮下脂肪组织中提取和拟合采样点来估计偏置场。然后，通过阈值检测和偏置场估计的细化来检测来自内脏脂肪组织隔室的附加采样点。将其与使用模拟腹部图像和真实 3T 数据的既定方法进行比较。
在低偏置场幅度（40-50%）下，模拟研究表明两种方法的平均变异系数（CV）都有很好的降低（均>80%）。在更高的幅度下，与 LEMS（54.3%）相比，我们的方法的 CV 降低明显更高（83.6%）。在真实数据研究中，我们的方法显示出可靠的不均匀性降低，而 LEMS 算法有时甚至会放大不均匀性。
即使在使用高场强和/或相控阵线圈获得的严重损坏的图像切片中，该方法也能利用简单的阈值技术，准确可靠地分割腹部脂肪组织。
