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pet-volume – Volume-based processing of PET images

This pipeline performs several processing 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 MR image using SPM;
  • (optional) partial volume correction (PVC) using the PETPVC toolbox [Thomas et al., 2016];
  • inter-subject spatial normalization of the PET image into MNI space based on the DARTEL deformation model of SPM [Ashburner, 2007];
  • 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. The list of available atlases can be found here.

Clinica & BIDS specifications for PET modality

Since Clinica v0.6, PET data following the official specifications in BIDS version 1.6.0 are now compatible with Clinica. See BIDS page for more information.

Prerequisite

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

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of either SPM12 and Matlab, or spm standalone.

In addition, if you want to apply partial volume correction (PVC) on your PET data, you will need to install PETPVC 1.2.4, which depends on ITK 4.

Running the pipeline

The pipeline can be run with the following command line:

clinica run pet-volume [OPTIONS] BIDS_DIRECTORY CAPS_DIRECTORY GROUP_LABEL ACQ_LABEL
                       {pons|cerebellumPons|pons2|cerebellumPons2}

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
  • GROUP_LABEL is the label of the group that is associated to the DARTEL template that you had created when running the t1-volume pipeline.
  • ACQ_LABEL is the label given to the PET acquisition, specifying the tracer used (trc-<acq_label>).
  • 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.

with specific options :

  • --pvc_psf_tsv: TSV file containing the psf_x, psf_y and psf_z of the PSF for each PET image. More explanation is given in PET Introduction page.

    Clinica v0.3.8+

    Since the release of Clinica v0.3.8, the handling of PSF information in the TSV file has changed: fwhm_x, fwhm_y, fwhm_z columns have been replaced by psf_x, psf_y, psf_z and the acq_label column has been added. Additionally, the SUVR reference region is now a compulsory argument: it will be easier for you to modify Clinica if you want to add a custom reference region (PET Introduction page). Choose cerebellumPons for amyloid tracers or pons for FDG to have the previous behavior.

  • --smooth: a list of integers specifying the different isotropic full width at half maximum (FWHM) in millimeters to smooth the image. Default value is: 0, 8 (both without smoothing and with an isotropic smoothing of 8 mm)

  • --reconstruction_method: Select only images based on a specific reconstruction method.
Optional parameters common to all pipelines
  • -tsv / --subjects_sessions_tsv

This flag allows you to specify in a TSV file the participants belonging to your subset. For instance, running the FreeSurfer pipeline on T1w MRI can be done using :

clinica run t1-freesurfer BIDS_PATH OUTPUT_PATH -tsv my_subjects.tsv

participant_id  session_id
sub-CLNC0001    ses-M000
sub-CLNC0001    ses-M018
sub-CLNC0002    ses-M000

Creating the TSV

To make the display clearer the rows here contain successive tabs but that should not happen in an actual TSV.

  • -wd / --working_directory

By default when running a pipeline, a temporary working directory is created. This directory stores all the intermediary inputs and outputs of the different steps of the pipeline. If everything goes well, the output directory is eventually created and the working directory is deleted.

With this option, a working directory of your choice can be specified. It is very useful for the debugging process or if your pipeline crashes. Then, you can relaunch it with the exact same parameters which will allow you to continue from the last successfully executed node. For the pipelines that generate many files, such as dwi-preprocessing (especially if you run it on multiple subjects), a specific drive/partition with enough space can be used to store the working directory.

  • -np / --n_procs

This flag allows you to exploit several cores of your machine to run pipelines in parallel, which is very useful when dealing with numerous subjects and multiple sessions. Thanks to Nipype, even for a single subject, a pipeline can be run in parallel by exploiting the cores available to process simultaneously independent sub-parts. We recommend using your_number_of_cpu - 1 for costly pipelines such as pet-surface-longitudinal.

If you do not specify -np / --n_procs flag, Clinica will detect the number of threads to run in parallel and propose the adequate number of threads to the user.

  • -cn / --caps-name

Use this option if you want to specify the name of the CAPS dataset that will be used inside the dataset_description.json file, at the root of the CAPS folder (see CAPS Specifications for more details). This works if this CAPS dataset does not exist yet, otherwise the existing name will be kept.

Several PET scans

It can happen that a BIDS dataset contains several PET scans for a given subject and session. In this situation, these images will differ through at least one BIDS entity like the tracer or the reconstruction method. When running the PET pipeline, clinica will raise an error if more than one image matches the criteria provided through the command line. To avoid that, it is important to specify values for these options such that a single image is selected per subject and session.

Tip

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

Outputs

Results are stored in the following folder of the CAPS hierarchy: subjects/<participant_id>/<session_id>/pet/preprocessing.

The main output files are:

  • <source_file>_space-T1w[_pvc-rbv]_pet.nii.gz: PET image registered into the T1-weighted MRI native space.

  • <source_file>_space-Ixi549Space[_pvc-rbv]_suvr-<label>_mask-brain[_fwhm-<X>mm]_pet.nii.gz: standard uptake value ratio (SUVR) PET image in MNI space, masked to keep only the brain, and optionally smoothed.

  • atlas_statistics/<source_file>_space-<space>[_pvc-rbv]_suvr-<label>_statistics.tsv: TSV files summarizing the regional statistics on the labelled atlas <space>.

Note

The [_pvc-rbv] label indicates whether the PET image has undergone partial value correction (region-based voxel-wise method) or not.

The full list of output files from the pet-volume pipeline can be found in the The ClinicA Processed Structure (CAPS) specifications.

Going further

Describing this pipeline in your paper

Example of paragraph:

These results have been obtained using the pet-volume pipeline of Clinica [Routier et al., 2021; Samper et al., 2018]. This pipeline first performs intra-subject registration of the PET image into the space of the subject’s T1-weighted MR image using SPM. [The PET image is corrected for partial volume effects using the PETPVC toolbox [Thomas et al., 2016].] 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 a reference region ([pons | pons + cerebellum]). Finally, the average PET uptake is computed for a set of regions obtained from different atlases in MNI space [Tzourio-Mazoyer et al., 2002, 2015; Joliot et al., 2015; Hammers et al., 2003; Gousias et al., 2008; Shattuck et al., 2008; CAT12].

Tip

Easily access the papers cited on this page on Zotero.

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