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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 `extract and deep learning classification algorithms presented in [Wen et al., 2020].


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:



  • 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 the option --uncropped_image if you do not want to crop the image.


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


Do not hesitate to type clinica run t1-linear --help to see the full list of parameters.


Results are stored in the following folder of the CAPS hierarchy: subjects/<participant_id>/<session_id>/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 use the ClinicaDL framework presented in [Wen et al., 2020] for classification or registration quality check based on deep learning methods.

Describing this pipeline in your paper

Example of paragraph

These results have been obtained using the t1-linear pipeline of Clinica [Routier et al., 2021; 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.


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