Skip to content

t1-volume-* – Pre-processing of T1-weighted MRI images for volume analyses

Four main pre-processing steps are performed on T1-weighted MRI images using the SPM software:

  • A: Tissue segmentation, bias correction and spatial normalization to MNI space This corresponds to the Segmentation procedure of SPM that simultaneously performs tissue segmentation, bias correction and spatial normalization, a procedure also known as "Unified segmentation" [Ashburner and Friston, 2005].

    Clinica pipeline: t1-volume-tissue-segmentation

  • B1: Create new Dartel template A group template is created using DARTEL, an algorithm for diffeomorphic image registration, from the subjects' tissue probability maps in native space (usually GM, WM and CSF tissues) obtained at the previous step. Here, not only the group template is obtained, but also the deformation fields from each subject's native space into the Dartel template space. This is achieved by wrapping the Run Dartel procedure from SPM [Ashburner, 2007].

    Clinica pipeline: t1-volume-create-dartel

  • B2: Reuse existing Dartel template Each subject T1-weighted MRI image is registered to the space of a previously existing Dartel template.

    Clinica pipeline: t1-volume-existing-dartel

  • C: Dartel template to MNI Once the transformation from the subject’s T1-weighted MRI image to the Dartel template has been computed, the T1-weighted MRI image of each subject can be transported to the MNI space. More precisely, for a given subject, its flow field into the Dartel template is combined with the transformation of the Dartel template into MNI space, and the resulting transformation is applied to the subject’s different tissue maps. As a result, all the images are in a common space, providing a voxel-wise correspondence across subjects. This is achieved by wrapping the Dartel2MNI procedure from SPM [Ashburner, 2007].

    Clinica pipeline: t1-volume-dartel2mnni

  • D: Atlas statistics A set of anatomical regions is obtained from different atlases in MNI space (list of available atlases here). The average gray matter density (also in MNI space) is then computed in each of the regions.

    Clinica pipeline: t1-volume-parcellation

These four pre-processing steps can be performed in one go with one of these two functions:

  • t1-volume-new-template = A + B1 + C + D (to create a new Dartel template)
  • t1-volume-existing-template = A + B2 + C + D (to reuse an existing Dartel template)

Dependencies

  • If you only installed the core of Clinica, this pipeline needs the installation of SPM and CAT12. You can find how to install these software packages on the third-party page.

Running the pipeline

The full pipelines (t1-volume-new-template and t1-volume-existing-template) can be run with the following command lines:

clinica run t1-volume-new-template bids_directory caps_directory group_id
clinica run t1-volume-existing-template bids_directory caps_directory group_id

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_id is the user-defined identifier for the provided group of subjects.

The sub-pipeline t1-volume-segmentation can be run with the following command line:

clinica run t1-volume-segmentation bids_directory caps_directory
t1-volume-create-dartel, t1-volume-existing-dartel, t1-volume-dartel2mni can be run with the following command line:
clinica run t1-volume-* bids_directory caps_directory group_id

Pipeline options:

  • -tissue_classes: a list of integers (possible values range from 1 to 6) that indicates the tissue classes to save after segmentation (in order: gray matter, GM; white matter, WM; cerebrospinal fluid, CSF; bone; soft-tissue; air/background). Default value is: 1, 2, 3 (GM, WM and CSF are saved).
  • -dartel_tissues: a list of integers (possible values range from 1 to 6) that indicates the tissue classes to use for the Dartel template calculation (in order: gray matter, GM; white matter, WM; cerebrospinal fluid, CSF; bone; soft-tissue; air/background). Default value is: 1, 2, 3 (GM, WM and CSF are used).
  • -fwhm: a list of integers specifying the different isomorphic full width at half maximum (fwhm) in millimeters used to smooth the images. Default value is: 0, 8 (both without smoothing and with an isomorphic smoothing of 8 mm).
  • -modulate: a boolean. If True output images are modulated and tissue count is preserved. If False they are not modulated and concentrations are preserved. Default value: True

Note

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

Tip

Do not hesitate to type t1-volume-* --help to see the full list of parameters.

Outputs

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

Tissue segmentation, bias correction and spatial normalization

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1/spm/segmentation.

The main output files are:

  • native_space/:
    • <source_file>_segm-[graymatter|whitematter|csf]_probability.nii.gz: The tissue probability maps for the gray matter, white matter and CSF.
  • normalized_space/
    • <source_file>_space-Ixi549Space_T1w.nii.gz: The T1-weighted image in MNI space.
    • <source_file>_segm-[graymatter|whitematter|csf]_space-Ixi549Space_modulated-on_probability.nii.gz: The modulated tissue probability maps, i.e. the tissue probability maps multiplied by their relative volume before and after spatial normalisation, into the MNI space.

Example of gray matter (GM), white matter (WM) and CSF tissue segmentation.

Dartel template

The final estimation of the gray matter template is stored under the following folder of the CAPS hierarchy: groups/group-<group_id>/t1/group-<group_id>_template.nii.gz

Example of a group template calculated using DARTEL. Only the gray matter class is shown.

The flow fields containing the deformation from an image to the group template are stored in the folder subjects/sub-<participant_label>/ses-<session_label>/t1/spm/dartel/group-<group_id>/ under the filename <source_file>_target-<group-id>_transformation-forward_deformation.nii.gz.

Dartel template to MNI

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1/spm/dartel/group-<group_id>.

The main output file is:

  • <source_file>_segm-[graymatter|whitematter|csf]_space-Ixi549Space_modulated-on_fwhm-<label>_probability.nii.gz: the different tissue maps that have been registered to the MNI space.

Final result: Probability Gray Matter in MNI space without smoothing (top) or smoothed using a 8 mm FWHM kernel (bottom).

Atlas statistics

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1/spm/dartel/group-<group_id>/atlas_statistics/.

The main output file is:

  • <source_file>_space-<space>_map-graymatter_statistics.tsv: TSV files summarizing the regional statistics on the labelled atlas .

Describing this pipeline in your paper

Example of paragraph for the t1-volume-new-template pipeline (short version) :

Theses results have been obtained using the t1-volume-new-template pipeline of Clinica. This pipeline is a wrapper of the Segmentation [Ashburner and Friston, 2005], Run Dartel [Ashburner, 2007] and Normalise to MNI Space routines implemented in SPM.

Example of paragraph for the t1-volume-new-template pipeline and each sub-pipeline (long version) :

Theses results have been obtained using the t1-volume-new-template pipeline of Clinica. This pipeline is a wrapper of the Segmentation, Run Dartel and Normalise to MNI Space routines implemented in SPM. First, the Unified Segmentation procedure [Ashburner and Friston, 2005] is used to simultaneously perform tissue segmentation, bias correction and spatial normalization of the input image. Next, a group template is created using DARTEL, an algorithm for diffeomorphic image registration [Ashburner, 2007], from the subjects’ tissue probability maps on the native space, usually GM, WM and CSF tissues, obtained at the previous step. The DARTEL to MNI method [Ashburner, 2007] is then applied, providing a registration of the native space images into the MNI space. Finally, a set of anatomical regions are obtained from different atlases in MNI space and the average gray matter density is computed in each of the regions.

Tip

Easily access the papers cited on this page on Zotero.