trislett / ants_tbss

TBSS (FSL) implementation with ANTs and T1w registration to template

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ants_tbss

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TBSS (FSL) implementation with ANTs and T1w registration to template. ants_tbss creates the TBSS skeleton using ANTS without FA to FA registrations.

There are essentially two steps: (a) Inter-modality, intrasubject registration of the B0 image to subject T1w image (b) Registration of subject T1w image the MNI152 1mm brain template (by default).

The software requires tight brain extractions for the T1w images. Brain extraction using antsBrainExtraction.sh and based on recommended settings from fMRIprep.

It is also possible to use ants_tbss (--othermodality) for registration of other modalities such as fMRI (e.g., betted example_func.nii.gz) to B0-in-native-T1w, and use the previously calculated transformations to native space and standard space.

voxel_slices is also installed for fast production of quality controls images.

e.g.,

Autothresholded FA on T1w image in MNI_1mm space Autothresholded FA on T1w image in MNI_1mm space

FA with MNI_1mm brain image auththresholded segmentation FA with MNI_1mm brain image segmentation

Skeletonized FA on mean FA in MNI_1mm space Skeletonized FA on mean FA in MNI_1mm space

Citation: Tustison NJ, Avants BB, Cook PA, Kim J, Whyte J, Gee JC, Stone JR. Logical circularity in voxel-based analysis: normalization strategy may induce statistical bias. Hum Brain Mapp. 2014 Mar;35(3):745-59. doi: 10.1002/hbm.22211.

Also read this post from the ANTS forum: https://sourceforge.net/p/advants/discussion/840261/thread/e6fc9a8c/

If you use the ants brain extraction script:

The script antsBrainExtraction.sh was used to perform brain extraction including N4BiasFieldCorrection and the OASIS template.

Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010 Jun;29(6):1310–20. doi:10.1109/TMI.2010.2046908.

Temporary cookbook for the lazy

Requirements

  • FSL, ANTs, parallel
  • Python libraries: numpy, argparse, nibabel, matplotlib, scipy, scikit-image

Installation

Create python environment (2.7 or 3.x)

virtualenv -p python3.7 python37env

source python37env/bin/activate

Clone the git page, and install ants_tbss

pip install git+https://github.com/trislett/ants_tbss.git

Make files list and run ants_tbss

Make a text file with betted B0 images

for i in $(cat subjects); do echo /path/to/images/${i}*B0*nii.gz; done > B0_brain_list

Make a text file with betted T1w images

for i in $(cat subjects); do echo /path/to/images/${i}*T1w_T1_BrainExtractionBrain.nii.gz; done > T1w_brain_list

Make a text file with betted B0 images

for i in $(cat subjects); do echo /path/to/images/${i}*FA*nii.gz; done > FA_list

Run ants_tbss

ants_tbss --antsregtotemplate B0_brain_list T1w_brain_list --runtbss FA_list FA -nlws --numthreads 8

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TBSS (FSL) implementation with ANTs and T1w registration to template

License:GNU General Public License v3.0


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