PET_Surface

pet-surface - Surface-based processing of PET images

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

• co-registration of PET and T1-w MRI (T1) 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].

Prerequisite

You need to have performed the t1-freesurfer pipeline on your T1 images.

Dependencies

If you only installed the core of Clinica, this pipeline needs the installation of FreeSurfer 6.0, SPM12, FSL 6.0 and PETPVC 1.2.4 (which depends on ITK 4) on your computer. You can find how to install these software packages on the third-party page.

Running the pipeline

The pipeline can be run with the following command line:

clinica run pet-surface <bids_directory> <caps_directory>

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.

If you want to run the pipeline on a subset of your BIDS dataset, you can use the -tsv flag to specify in a TSV file the participants belonging to your subset.

Please note that next to each PET file in your BIDS folder, a json file must be added to specify the EffectiveResolutionInPlane and EffectiveResolutionAxial in mm relative to the point spread function (PSF).

Your BIDS hierarchy (for a given subject sub-001) must look like this:

bids
└── sub-001
    └── ses-M00
        ├── anat
        │   └── sub-001_ses-M00_T1w.nii.gz
        └── pet
            ├── sub-001_ses-M00_task-rest_acq-fdg_pet.json
            └── sub-001_ses-M00_task-rest_acq-fdg_pet.nii.gz

The sub-001_ses-M00_task-rest_acq-fdg_pet.json must look like this:

{
	"Psf":[
		{
			"EffectiveResolutionInPlane": 5.5,
			"EffectiveResolutionAxial": 5.5
		}
	]
}

Pipeline options:

• --pet_tracer: type of PET image to process. Possible values are fdg and av45. Default value is fdg. This parameter affects the reference region used for the intensity normalization (FDG: pons, AV45: pons and cerebellum).

• -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).

3) 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 --help to see the full list of parameters.

Outputs

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

The files are (where * stands for sub-<participant_label>_ses-<session_label>):

• atlas_statistics/*_task-<label>_acq-<label>_pet_space-<label>_pvc-iy_suvr-<label>_statistics.tsv: TSV files summarizing the regional statistics on the labelled atlases (Desikan and Destrieux).
• *_hemi-{left|right}_midcorticalsurface: surface at equal distance between the white matter/gray matter interface and the pial surface (one per hemisphere).
• *_task-rest_acq-<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 pipeline).

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

 *FDG PET SUVR projected onto the cortical surface (left hemisphere) for (from left to right) a cognitively normal subject (CN), a patient with Alzheimer’s disease (AD), a patient with semantic variant primary progressive aphasia (svPPA) and a patient with logopenic variant primary progressive aphasia (lvPPA). The first row is the projection in the subject’s space. The second row is the same signal for each subject, but warped to FsAverage after smoothing with a 20 mm Gaussian kernel.*

Going further

• You can use projected PET data to perform group comparison or correlation analysis with the statistics-surface pipeline.
• You can use projected PET data to perform classification based on machine learning, as showcased in the AD-ML framework.

Describing this pipeline in your paper

!!! cite "Example of paragraph:" These results have been obtained using the pet-surface pipeline of Clinica [Routier et al; Marcoux et al., 2018]. The subject’s PET image was registered to the T1 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, 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 Easily access the papers cited on this page on Zotero.

Support

• You can use the Clinica Google Group to ask for help!
• Report an issue on GitHub.

Appendix I: Diagram of the pipeline execution

 *The subject's T1-w MRI is coregistered with the PET image and the PET image is intensity normalized using the average uptake in a reference region. In parallel, cortical surfaces and a parcellation are generated from the subject's T1-w MRI. The PET image, after partial volume correction performed using the parcellation, is robustly projected onto the cortical surface. Finally, regional mean uptake values are extracted from the projected PET data, and the projected PET signal in the subject's native space is spatially normalized to the standard space of FsAverage.*

Appendix II: How to manipulate outputs

Outputs of pipeline are composed of two different type of file: surface files and MGH data that are to be overlaid onto a surface.

Surface file

They can be read using various software. You can open it using freeview (FreeSurfer viewer), with freeview -f /path/to/your/surface/file.

You can also open it in MATLAB, using SurfStat: mysurface = SurfStatReadSurf('/path/to/your/surface/file'). This will give you a structure with fields coord (for coordinates), a list of coordinates for each point of the mesh, and also field tri (for triangle), a list of triplet for each triangle, each number representing the Nth vertex of the coord list. Here is below an example to make things clearer (read with Matlab)


Data files

The data files wear the .mgh extension. It is composed of a single vector. This file contains a vector, where value at Nth position must be mapped into the Nth vertex of the coord list to be correctly represented. You can access them either in Matlab with the command:

mydata = SurfStatReadData('/path/to/your/file.mgh');

(you will get a single row vector)

Or in Python with the nibabel library:

import nibabel
mydata = nibabel.load('/path/to/your/mgh/file')

mydata will then be a MGHImage, more information here. Keep in mind that if you want to manipulate the data vector within this object, you will need to transform it a bit. Indeed, if you do the following:

raw_data = mydata.get_data()
print(raw_data.shape)

The shape of your "raw" vector will probably look like this: (163842, 1, 1). Use the squeeze function from numpy to get a (163842,) shape. (documentation here). The reverse operation ((163842,) to a (163842, 1, 1) shape) can be achieved with the atleast_3d function from numpy (documentation here). This may come handy when you need to create a MGHImage from scratch.

Visualization of the results

After the execution of the pipeline, you can check the outputs of one subject by running this command (subject moved into FsAverage):

freeview -f $SUBJECTS_DIR/fsaverage/surf/lh.pial:overlay=path/to/your/projected/pet/in/fsaverage/left/hemi \
 -f $SUBJECTS_DIR/fsaverage/surf/rh.pial:overlay=path/to/your/projected/pet/in/fsaverage/right/hemi


But you can also visualize your subjects cortical projection directly into his native space:

freeview -f path/to/midcortical/surface/left:overlay=path/to/your/projected/pet/in/nativespace/left/hemi \
 -f path/to/midcortical/surface/right:overlay=path/to/your/projected/pet/in/nativespace/right/hemi

You will need to adjust the colormap using the Configure button in the left panel, just below the Overlay section.

You can also visualize your surface using the SurfStat tool. Once SurfStat installation folder is added to your MATLAB path, you can display your surfaces with the following commands:

mydata = SurfStatReadData({'/path/to/left/data', '/path/to/right/data'});
mysurfaces = SurfStatReadSurf({'/path/to/left/surface', '/path/to/right/surface'});
figure, SurfStatViewData(mydata, mysurfaces, 'Title of figure');

It will get you the following figure: