T1_Linear

t1-linear - Affine registration of T1w images to the MNI standard space

This pipeline performs a set of steps in order to affinely align T1-weighted MR images to the MNI space using the ANTs software package [Avants et al., 2014]. These steps include: bias field correction using N4ITK [Tustison et al., 2010]; affine registration to the MNI152NLin2009cSym template [Fonov et al., 2011, 2009] in MNI space with the SyN algorithm [Avants et al., 2008]; cropping of the registered images to remove the background.

This pipeline was designed as a prerequisite for the deeplearning-prepare-data pipeline and deep learning classification algorithms presented in [Wen et al., 2020].

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of ANTs on your computer. You can find how to install this software package on the third-party page.

Running the pipeline

The pipeline can be run with the following command line:

clinica run t1-linear <bids_directory> <caps_directory>

where:

• bids_directory is the input folder containing the dataset in a BIDS hierarchy.
• caps_directory is the output folder containing the results in a CAPS hierarchy.

On default, cropped images (matrix size 169×208×179, 1 mm isotropic voxels) are generated to reduce the computing power required when training deep learning models. Use --uncropped_image flag if you do not want to crop the image.

!!! note The arguments common to all Clinica pipelines are described in Interacting with clinica.

Outputs

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1_linear with the following outputs:

• <source_file>_space-MNI152NLin2009cSym_res-1x1x1_T1w.nii.gz: T1w image affinely registered to the MNI152NLin2009cSym template.
• (optional) <source_file>_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz: T1w image registered to the MNI152NLin2009cSym template and cropped.
• <source_file>_space-MNI152NLin2009cSym_res-1x1x1_affine.mat: affine transformation estimated with ANTs.

Going further

• You can now run the deeplearning-prepare-data pipeline to prepare images to be used with the PyTorch library [Paszke et al., 2019] for classification based on deep learning using the AD-DL framework presented in [Wen et al., 2020].

Describing this pipeline in your paper

!!! cite "Example of paragraph" These results have been obtained using the t1-linear pipeline of Clinica [Routier et al; Wen et al., 2020]. More precisely, bias field correction was applied using the N4ITK method [Tustison et al., 2010]. Next, an affine registration was performed using the SyN algorithm [Avants et al., 2008] from ANTs [Avants et al., 2014] to align each image to the MNI space with the ICBM 2009c nonlinear symmetric template [Fonov et al., 2011, 2009]. (Optional) The registered images were further cropped to remove the background resulting in images of size 169×208×179, with 1 mm isotropic voxels.

!!! tip Easily access the papers cited on this page on Zotero.