处理小鼠MRI vbm中出现一些问题

近期在处理小鼠VBM数据,参考的是这篇文章的教程《Brain structure and synaptic protein expression alterations after antidepressant treatment in a Wistar–Kyoto rat model of depression》,其中内容为:The T2-weighted images analysis was performed using Statistical Parametric Mapping 12 (SPM12; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) and Data Processing & Analysis of Brain Imaging (DPABI) (Yan et al., 2016). First, each T2-weighted image was transformed from their original format of DICOM to NIFTI and re-sized voxels by a factor of 10 so that the rat brain volume will resemble the human brain and match the most parameters in SPM12. Then the rat brain was manually extracted from surrounding skull tissue and refined with a skull stripping tool in BrainSuite (http://brainsuite.org). After brain extraction, each rat brain was co-registered to SIGMA template (Barrière et al., 2019) and re-sampled into 1.5 mm voxels. Next, each image was segmented into three tissue priors (Gray Matter, White Matter, Cerebrospinal Fluid maps) using the old segment tool in SPM12. In this step, the default tissue probability maps were replaced by the SIGMA template’s GM, WM, and CSF tissue maps. To perform a more accurate analysis, all the segment priors were used to generate a subject-specific template by DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) algorithm in SPM12 as follows: 1 the “seg_sn.mat” documents generated during the old segment were used for initial import step; 2 GM, WM and CSF templates were created by DARTEL using imported three tissue priors respectively; 3 the Jacobian images acquired during DARTEL template creation of GM priors were applied to warp individual T2-weighted images; 4 the normalized T2-weighted images were averaged to generate final subject-specific template; 5 the GM priors were normalized to a subject-specific template and modulated. Finally, the normalized GM maps were smoothed using an 8-mm FWHM Gaussian kernel.

在进行old segment中,我不知道这里affine regularisation应该选择什么比较好,文章也没有写,而且发现用no affine registration和ICBM space template差别还挺大的,希望老师能给予意见,要是有懂小鼠VBM和DTI数据处理的,我可以给予付费咨询,也请联系,谢谢

图片如图所示

据我所知,这个选项的目的是考虑到ICBM模板比个体的脑要大一些,好像是因为ICBM模板是根据线性配准构建的,具体的你可以看这个选项下面的help信息,有详细解释。如果你的老鼠模板大小是正常的,就没有必要选ICBM space template。至于应该选择哪个,我个人觉得还要考虑实际的分割效果,看看哪个选项分割更准确。另外,我不了解的是,为什么要用old segment,很明显segment模块要更好一些。

老师,您好,用old segment的原因是目前小鼠的脑模板都是灰质 白质 脑脊液三个3d文件,正好可以替换old segment中人的三个3d文件,而segment中是6个3d文件组成的4d TPM文件,我没办法进行替换,老师您有什么好办法吗

明白了,模板文件只有三个组织的prior,而segment模块需要6个。那就没办法了,只能这样了。或者就是考虑其他软件了。