2015年6月29日 星期一

6.29 TBSS manual

预处理 -> 校准 -> 后校准 -> 预统计
1-tbss_1_preproc: make directory -> put files into directory -> scale FA values -> check scaling -> create an exploratory webpage for QC (5 steps);
2-tbss_2_reg: non-linear registration, through either: 1) pre-defined target (FMRIB58_FA); 2) self-selected target; 3) study-specific target (MNI152 standard space). Can be time consuming;
3-tbss_3_postreg: non-linear transformation -> 4D image file -> mean FA -> check threshold -> skeletonization (5 steps);
4-tbss_4_prestats: threshold the mean FA skeleton -> create a distance map -> create a 4D image file containing the skeletonized FA data
  • (Smith 2006) distance map: all voxels in the image are filled with a value encoding the distance to the nearest skeleton point.
    There are two limits placed on this perpendicular search within a given subject's FA image. The first is that we constrain the search to remain closer to the starting section of skeleton than to any other section of skeleton; where two separate sections of the skelton lie close to each other, the space in between is divided into two, and each skeleton section can only search voxels within its part of that space.
    Secondly, there is a further constraint placed on the maximum search distance via a soft distance limit. A wide Gaussian function (FWHM 20 mm) is applied as a multiplicative weighting to FA values when carrying out the search for maximum FA (not: this is a weighting function in the search, not a smoothing). This deweights the most distant voxels in a smooth, controlled manner. Once the optimal voxel has been found, its FA value (not weighted by the distance function) is placed into the current skeleton voxel. I am not fully understand this part of the algorithm.

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