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pet-linear - Linear processing of PET images

This pipeline performs spatial normalization to the MNI space and intensity normalization of PET images. Its steps include:

  • affine registration to the MNI152NLin2009cSym template [Fonov et al., 2011, 2009] in MNI space with the SyN algorithm [Avants et al., 2008] from the ANTs software package [Avants et al., 2014];
  • intensity normalization using the average PET uptake in reference regions resulting in a standardized uptake value ratio (SUVR) map;
  • cropping of the registered images to remove the background.


You need to have performed the t1-linear pipeline on your T1-weighted MR images.


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;
  • ACQ_LABEL is the label given to the PET acquisition, specifying the tracer used (acq-<acq_label>). It can be for instance 'fdg' for 18F-fluorodeoxyglucose or 'av45' for 18F-florbetapir;
  • The reference region is used to perform intensity normalization (i.e. dividing each voxel of the image by the average uptake in this region) resulting in a standardized uptake value ratio (SUVR) map. It can be cerebellumPons or cerebellumPons2 (used for amyloid tracers) and pons or pons2 (used for FDG). See PET introduction for more details about masks versions.

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

The pipeline also offers the possibility to save the PET image in the T1w space after rigid transformation using the --save_pet_in_t1w_space option.


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


Do not hesitate to type clinica run pet-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>/pet_linear.

The main output files are:

  • <source_file>_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_suvr-<label>_pet.nii.gz: PET SUVR image registered to the MNI152NLin2009cSym template and cropped.
  • <source_file>_space-T1w_rigid.mat: rigid transformation between the PET and T1w images estimated with ANTs.
  • (optional) <source_file>_space-MNI152NLin2009cSym_res-1x1x1_pet.nii.gz: PET SUVR image affinely registered to the MNI152NLin2009cSym template (i.e. not cropped).
  • (optional) <source_file>_space-T1w_pet.nii.gz: PET image affinely registered to the associated T1w image.

Going further

You can now use the ClinicaDL framework presented in [Wen et al., 2020] for classification based on deep learning methods.

Describing this pipeline in your paper

Example of paragraph

These results have been obtained using the pet-linear pipeline of Clinica [Routier et al., 2021]. This pipeline first performs intra-subject rigid registration of the PET image into the space of the subject’s T1-weighted (T1w) MR image using the SyN algorithm [Avants et al., 2008] from ANTs [Avants et al., 2014]. The PET to T1w transformation is then composed with T1w to ICBM 2009c nonlinear symmetric template affine transformation, obtained with t1-linear, to transport the PET image to the MNI space [Fonov et al., 2011, 2009]. The PET image is further intensity normalized using the average PET uptake in a reference region ([pons | pons + cerebellum]), resulting in a standardized uptake value ratio (SUVR) map. The PET SUVR image in MNI space is finally cropped to remove the background, resulting in images of size 169×208×179 with 1 mm isotropic voxels.


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