1. Using DICOM sorter in DPABI’s Utilities to arrange the raw data, and then use DPARSFA to transfer the DICOM files, either T1 or Fun images, into NIFTI files.
2. Reorient T1Img according to the spmmouse’s grey matter template (grer62.nii) by utilizing spm5 and spmmouse. (Note: spmmouse only can work on spm5 and a version of matlab earlier than 2014a)
3. Using spmmouse to do segment on T1 images. (Note: spmmouse may not load the path of templates correctly, it should be checked and load the templates manually.)
4. Using the matrix (name_seg_sn.mat) got from the segment to perform normalize for all T1 images by using spmmouse.
5. Using Image Calculator (in DPABI’s Utilities) to get a mean image from all normalized T1 images. [expression: mean(g1)]
6. Augment the voxel size of the T1 images, the Fun images and the mean T1 image to 10 times by utilizing Voxel Size Augmentor (in DPABI’s Utilities).
7. Resliced the mean T1 image by using an original T1 image as a reference.
8. For this resliced mean T1 image, using MRIcron to draw a mask to cover the brain and using Image Calculator to strip the skull part.(expression: i1.*i2, i2=mask image). Then using this image to replace the T1 template for DPARSF for Rat Data.
9. Using MRIcron draw a mask of a random T1 image, to which all the other T1 images are coregistered. Then using the mask to strip the skull of all the T1 images.
10. Realign and reorient Fun images by using the DPARSF for Rat Data, and strip the skull of the images in RealignParameter folder one by one manually.
11. Using DPARSF for Rat Data to do T1 coreg to Fun, and check the quality of the coregister one by one. (note: sometimes the bad coregister may cause by incomplete striping of skull, and re-draw a mask can fix it)
12. Run the following procedure in DPARSF for Rat Data (choose Normalize by using T1 image templates) to get ALFF, fALFF, ReHo, etc. (note: for Normalize, a suitable Bounding Box is recommended)
Excellent! Thanks for sharing
Excellent! Thanks for sharing, Jiesi!
Best,
Chao-Gan