Segmentation of MRI brain scans using non-uniform partial volume densities
Segmentation of MRI brain scans using non-uniform partial volume densities
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DOI：
10.1016/j.neuroimage.2009.07.041
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发表时间：
2010-01-01
期刊：
影响因子：
5.7
通讯作者：
Schnack, Hugo G.
中科院分区：
文献类型：
--
作者：
Brouwer, Rachel M.;Pol, Hilleke E. Hulshoff;Schnack, Hugo G.
我们提出了一种算法，提供了一个部分体积分割的T1加权图像的大脑灰质，白色物质和脑脊液。该算法采用了非均匀的部分体积密度，考虑到弯曲的性质的皮质。纯灰色和白色物质的强度估计从图像中，使用扫描仪噪声和皮质部分容积效应。随后在每个体素中计算预期的组织分数。该算法已被测试的可靠性，正确估计的纯组织强度的真实的（重复）MRI数据和模拟（脑）图像。对于来自同一扫描仪的重复扫描以及来自具有不同场强的不同扫描仪的具有不同体素尺寸的扫描，三种组织类型的所有体积的类内相关系数（ICC）均高于0.93。与均匀部分体积密度相比，我们的非均匀部分体积密度的实现提供了更可靠的体积和组织分数。将该算法应用于模拟图像，结果表明，该算法能够准确地估计出纯组织的强度。皮质厚度的变化并不影响体积估计的准确性，这是一个有价值的属性时，研究（可能的）组的差异。总之，我们提出了一种新的部分体积分割算法，允许扫描仪和体素大小的比较。(C)2009 Elsevier Inc. All rights reserved.
We present an algorithm that provides a partial volume segmentation of a T1-weighted image of the brain into gray matter, white matter and cerebrospinal fluid. The algorithm incorporates a non-uniform partial volume density that takes the curved nature of the cortex into account. The pure gray and white matter intensities are estimated from the image, using scanner noise and cortical partial volume effects. Expected tissue fractions are Subsequently computed in each voxel. The algorithm has been tested for reliability, correct estimation of the pure tissue intensities on both real (repeated) MRI data and on simulated (brain) images. Intra-class correlation coefficients (ICCs) were above 0.93 for all volumes of the three tissue types for repeated scans from the same scanner, as well as for scans with different voxel sizes from different scanners with different field strengths. The implementation of our non-uniform partial volume density provided more reliable volumes and tissue fractions, compared to a uniform partial volume density. Applying the algorithm to simulated images showed that the pure tissue intensities were estimated accurately. Variations in cortical thickness did not influence the accuracy of the Volume estimates, which is a valuable property when studying (possible) group differences. In conclusion, we have presented a new partial Volume segmentation algorithm that allows for comparisons over scanners and voxel sizes. (C) 2009 Elsevier Inc. All rights reserved.