Du Yiping P, Jin Zhaoyang
Department of Psychiatry, University of Colorado Denver, School of Medicine, Denver, Colorado 80045, USA.
J Magn Reson Imaging. 2009 Oct;30(4):722-31. doi: 10.1002/jmri.21910.
To develop a robust algorithm for tissue-air segmentation in magnetic resonance imaging (MRI) using the statistics of phase and magnitude of the images.
A multivariate measure based on the statistics of phase and magnitude was constructed for tissue-air volume segmentation. The standard deviation of first-order phase difference and the standard deviation of magnitude were calculated in a 3 x 3 x 3 kernel in the image domain. To improve differentiation accuracy, the uniformity of phase distribution in the kernel was also calculated and linear background phase introduced by field inhomogeneity was corrected. The effectiveness of the proposed volume segmentation technique was compared to a conventional approach that uses the magnitude data alone.
The proposed algorithm was shown to be more effective and robust in volume segmentation in both synthetic phantom and susceptibility-weighted images of human brain. Using our proposed volume segmentation method, veins in the peripheral regions of the brain were well depicted in the minimum-intensity projection of the susceptibility-weighted images.
Using the additional statistics of phase, tissue-air volume segmentation can be substantially improved compared to that using the statistics of magnitude data alone.
利用图像的相位和幅度统计信息，开发一种用于磁共振成像（MRI）中组织-空气分割的稳健算法。
构建基于相位和幅度统计的多变量测量方法用于组织-空气体积分割。在图像域的3×3×3内核中计算一阶相位差的标准差和幅度的标准差。为提高区分精度，还计算了内核中相位分布的均匀性，并校正了由场不均匀性引入的线性背景相位。将所提出的体积分割技术的有效性与仅使用幅度数据的传统方法进行比较。
在合成体模和人脑的 susceptibility加权图像的体积分割中，所提出的算法显示出更有效和稳健。使用我们提出的体积分割方法，在susceptibility加权图像的最小强度投影中，大脑周边区域的静脉得到了很好的描绘。
与仅使用幅度数据的统计信息相比，利用相位的附加统计信息可显著改善组织-空气体积分割。
