pet-preprocess-volume – SPM-based pre-processing of PET imagesLink

This pipeline performs several preprocessing steps on PET data in voxel space, which include:

  • intra-subject registration of the PET image into the space of the subject’s T1-weighted MRI image using SPM;
  • (optional) partial volume correction (PVC) using the PETPVC toolbox;
  • inter-subject spatial normalization of the PET image into MNI space based on the DARTEL deformation model of SPM;
  • intensity normalization using the average PET uptake in reference regions resulting in a standardized uptake value ratio (SUVR) map;
  • parcellation into anatomical regions based on an atlas and computation of average values within each region.

PrerequisiteLink

You need to have performed the t1-spm-full-prep pipeline on your T1-weighted MRI images.

DependenciesLink

  • If you only installed the core of Clinica, this pipeline needs the installation of SPM. You can find how to install this software on the installation page.

  • If you want to apply partial volume correction (PVC) on your PET data, you will need to install PETPVC, which depends on ITK. More information on the installation page.

Running the pipelineLink

The pipeline can be run with the following command line:

clinica run pet-preprocess-volume bids_directory caps_directory id_group
where:

  • bids_directory is the input folder containing the dataset in a BIDS hierarchy.
  • caps_directory acts both as an input folder (where the results of the t1-spm-full-prep pipeline are stored) and as the output folder containing the results in a CAPS hierarchy.
  • id_group is the id of the group that is associated to the DARTEL template that you had created when running the t1-spm-full-prep pipeline. For more information check Interacting with clinica.

To correct for partial volume effects), the pipeline uses the region-based voxel-wise (RBV) correction). You will have to specify in a TSV file the full width at half maximum (FWHM), in millimeters, of the point spread function (PSF) associated with your data, in the x, y and z directions. For instance, if the FWHM of the PSF associated with your first image is 8 mm along the x axis, 9 mm along the y axis, and 10 mm along z axis, the first row of your TSV file will look like this:

participant_id    session_id     fwhm_x    fwhm_y    fwhm_z
sub-CLNC0001      ses-M00        8    9    10
sub-CLNC0002      ses-M00        7    6    5
sub-CLNC0003      ses-M00        6    6    6

The pipeline can then be run with the following command line:

clinica run pet-preprocess-volume bids_directory caps_directory id_group -fwhm <FWHM_TSV>

OutputsLink

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/pet/preprocessing.

The full list of output files from FSL pipeline can be found in the The ClinicA Processed Structure (CAPS) Specification.

Describing this pipeline in your paperLink

Example of paragraph (without PVC):

Theses results have been obtained using the pet-preprocess-volume pipeline of Clinica. This pipeline first performs intra-subject registration of the PET image into the space of the subject’s T1-weighted MRI image using SPM. The PET image is then spatially normalized into MNI space using the DARTEL deformation model of SPM and intensity normalized using the average PET uptake in reference regions. Finally, the PET image is parcellated into anatomical regions based on an atlas and average values are computed within each region.

Example of paragraph (with PVC):

Theses results have been obtained using the pet-preprocess-volume pipeline of Clinica. This pipeline first performs intra-subject registration of the PET image into the space of the subject’s T1-weighted MRI image using SPM. The PET image is then corrected for partial volume effects using the PETPVC toolbox. Next, the PET image is spatially normalized into MNI space using the DARTEL deformation model of SPM and intensity normalized using the average PET uptake in reference regions. Finally, the PET image is parcellated into anatomical regions based on an atlas and average values are computed within each region.