Applying and Inverting Transformations [on mask]

Posted 2021-02-22 yusani

cr: http://fsl.fmrib.ox.ac.uk/fslcourse/2019_Beijing/lectures/Registration/FSL_Registration_Practical.pdf

the story starts from a question...

If we were interested in the functional signal in (for example) the amygdala within the fMRI data in a particular subject, which space would we transform a standard mask of the amygdala into for further processing?

• The structural space - we want to make sure we get the most accurate signal from the data, and this is when the image is in structural space

• The distortion-corrected functional space - we do our task analysis in functional space, once it has been corrected for distortions

• The native functional space - we do our task analysis in functional space, without any registrations applied

the correct answer is no.2. but how come?

 

0.1. get familiar with transformation files

• highres2standard.mat
• highres2standard_warp.nii.gz
• ...

0.2 three main spaces in a FEAT analysis

• functional (represented by example_func);
• structural (represented by highres);
• MNI (represented by standard)

 

 

1. Creating an example mask [in MNI space] [from existing Atlases]

fsleyes -std &

......gui acts

2. Inverting a transform [to get std2highres_warp)

invwarp -w highres2standard_warp -o standard2highres_warp -r highres

3. Applying a transformation

applywarp -i LeftHippMask -r example_func -o LeftHippMaskFunc -w standard2highres_warp --postmat=highres2example_func.mat

 

4. Thresholding the Mask [now in func space] [to make it binary]

4.1 to choose 1)0.9, tighter; 2)0.5, of similar size/volume as before; 3)0.1, ok to include neighbouring stuctures.

4.2 

fslmaths LeftHippMaskFunc -thr 0.9 -bin LeftHippMaskFuncBin

5. Using the Mask

e.g. 

fslmeants -i ../filtered_func_data -m LeftHippMaskFuncBin

【Q】why filtered_func_data, the pre-processed fMRI data