Spatial normalization of brain images with focal lesions using cost function masking
Spatial normalization of brain images with focal lesions using cost function masking
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DOI：
10.1006/nimg.2001.0845
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发表时间：
2001-08-01
期刊：
影响因子：
5.7
通讯作者：
Ashburner, J
中科院分区：
文献类型：
--
作者：
Brett, M;Leff, AP;Ashburner, J
在对患有局灶性脑损伤的患者的研究中，通常可以将患者大脑的图像与其他受试者或标准模板的图像相结合。我们将此过程称为空间归一化。空间归一化可以改善神经心理学研究中病变位置的表现和分析。它还可以允许将其他数据（例如功能成像）与其他患者或正常对照组的数据进行比较。在功能成像中，空间归一化的标准过程是使用自动化算法，该算法根据图像强度值最小化图像和模板之间的差异度量。这些算法通常优化线性（翻译，旋转，变焦和剪切）和非线性变换。在存在局灶性病变的情况下，自动化算法试图减少病变位点模板和图像之间的图像错配。这可能会导致明显的不适当的图像失真，尤其是在使用非线性变换时。一种解决方案是使用成本功能掩盖掩盖图像差异中使用的区域以排除病变区域，以使病变不会偏向转化。我们使用局灶性病变和带有模拟病变的正常大脑的大脑选择的正常化介绍和评估了这一技术。我们的结果表明，成本功能掩蔽优于解决此问题的标准方法，即仅仿射归一化。我们建议，应常规使用成本功能掩盖来用于患有局灶性病变的大脑的正常化。 （c）2001学术出版社。
In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking-masking the areas used in the calculation of image difference to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions. (C) 2001 Academic Press.