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pet-surface-longitudinal - Surface-based longitudinal processing of PET images

This pipeline performs several processing steps for the longitudinal analysis of PET data on the cortical surface [Marcoux et al., 2018]:

  • co-registration of PET and T1-weighted MR images;
  • intensity normalization;
  • partial volume correction;
  • robust projection of the PET signal onto the subject’s cortical surface;
  • spatial normalization to a template;
  • atlas statistics.

This pipeline relies mainly on tools from FreeSurfer and PETPVC [Thomas et al., 2016].

The only difference with the pet-surface pipeline is that the subject’s cortical surface is obtained with the t1-freesurfer-longitudinal pipeline and not the t1-freesurfer pipeline.

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-freesurfer-longitudinal pipeline on your T1-weighted MR images.

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of FreeSurfer 6.0, FSL 6.0, and PETPVC 1.2.4 (which depends on ITK 4) on your computer.

In addition, you also need to either install SPM12 and Matlab, or spm standalone.

Running the pipeline

The pipeline can be run with the following command line:

clinica run pet-surface-longitudinal [OPTIONS] BIDS_DIRECTORY CAPS_DIRECTORY ACQ_LABEL
                                     {pons|cerebellumPons|pons2|cerebellumPons2} PVC_PSF_TSV

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-freesurfer-longitudinal pipeline are stored) and as the output folder containing the results in a CAPS hierarchy.
  • 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) orponsorpons2` (used for FDG).
  • PVC_PSF_TSV is the 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.

Info

Since the release of Clinica v0.3.8, the handling of PSF information has changed. In previous versions of Clinica, each BIDS-PET image had to contain a JSON file with the EffectiveResolutionInPlane and EffectiveResolutionAxial fields corresponding to the PSF in mm. EffectiveResolutionInPlane is replaced by both psf_x and psf_y, EffectiveResolutionAxial is replaced by 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.

Pipeline options:

  • -np: This parameter specifies the number of threads to run in parallel. We recommend using your_number_of_cpu - 1. Please note that PETPVC is extremely demanding in terms of resources and may cause the pipeline to crash if many subjects happen to be partial volume corrected at the same time (Error : Failed to allocate memory for image). To mitigate this issue, you can do the following:

    1) Use a working directory when you launch Clinica.

    2) If the pipeline crashed, just launch again the command (while giving the same working directory). The whole processing will continue where it left (you can reduce the number of threads to run in parallel the second time).

Note

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

Tip

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

Known error on macOS

If you are running pet-surface-longitudinal on macOS, we noticed that if the path to the CAPS is too long, the pipeline fails when the gtmseg command from FreeSurfer is executed. This generates crash files with gtmseg in the filename, for instance:

$ nipypecli crash crash-20210404-115414-sheldon.cooper-gtmseg-278e3a57-294f-4121-8a46-9975801f24aa.pklz
[...]
Abort
ERROR: mri_gtmseg --s sub-ADNI011S4105_ses-M000 --usf 2 --o gtmseg.mgz --apas apas+head.mgz --no-subseg-wm --no-keep-cc --no-keep-hypo
gtmseg exited with errors
Standard error:
Saving result to '<caps>/subjects/sub-ADNI011S4105/ses-M000/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M000/tmp/tmpdir.xcerebralseg.50819/tmpdir.fscalc.53505/tmp.mgh' (type = MGH )                       [ ok ]
Saving result to '<caps>/subjects/sub-ADNI011S4105/ses-M000/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M00/tmp/tmpdir.xcerebralseg.50819/tmpdir.fscalc.53727/tmp.mgh' (type = MGH )                       [ ok ]
Saving result to '<caps>/subjects/sub-ADNI011S4105/ses-M000/t1/freesurfer_cross_sectional/sub-ADNI011S4105_ses-M000/tmp/tmpdir.xcerebralseg.50819/tmpdir.fscalc.53946/tmp.mgh' (type = MGH )                       [ ok ]
Return code: 1

This is under investigation (see Issue #119 for details) and will be solved as soon as possible.

Case where several longitudinal IDs are present

If a subject has more than two longitudinal IDs (e.g. long-M000M018 and long-M000M018M036), Clinica is not currently able to deal with this particular case. This will be fixed in the future.

Outputs

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

The main output files are (where * stands for <participant_id>_<session_id>_<long_id>):

  • *_trc-<label>_pet_space-<label>_suvr-<label>_pvc-iy_hemi-<label>_fwhm-<value>_projection.mgh: PET data that can be mapped onto meshes. If the space is fsaverage, it can be mapped either onto the white or pial surface of FsAverage. If the space is native, it can be mapped onto the white or pial surface of the subject’s surface (i.e. {l|r}h.white, {l|r}h.pial files from the t1-freesurfer-longitudinal pipeline).
  • *_hemi-{left|right}_midcorticalsurface: surface at equal distance between the white matter/gray matter interface and the pial surface (one per hemisphere).
  • atlas_statistics/*_trc-<label>_pet_space-<label>_pvc-iy_suvr-<label>_statistics.tsv: TSV files summarizing the regional statistics on the labelled atlases (Desikan and Destrieux).

Note

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

Describing this pipeline in your paper

Example of paragraph:

These results have been obtained using the pet-surface-longitudinal pipeline of Clinica [Routier et al., 2021; Marcoux et al., 2018]. The subject’s PET image was registered to the T1-weighted MRI using spmregister (FreeSurfer) and intensity normalized using the [pons | pons and cerebellum] from the Pick atlas in MNI space as reference region (registration to MNI space was performed using SPM12). Partial volume correction was then performed using the iterative Yang algorithm implemented in PETPVC [Thomas et al., 2016] with regions obtained from gtmseg (FreeSurfer). Based on the subject’s white surface and cortical thickness estimated with the longitudinal pipeline of FreeSurfer [Fischl et al., 2012] (t1-freesurfer-longitudinal), seven surfaces for each hemisphere were computed, ranging from 35% to 65% of the gray matter thickness. The partial volume corrected data were projected onto these meshes and the seven values were averaged, giving more weight to the vertices near the center of the cortex. Finally, the projected PET signal in the subject’s native space was spatially normalized to the standard space of FsAverage (FreeSurfer).

Tip

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