Skip to content

quality-check - Evaluate registration quality

Two different quality check procedures are available in ClinicaDL: one for the t1-linear preprocessing pipeline and another for the t1-volume pipeline.

quality-check t1-linear - Evaluate t1-linear registration

The quality check procedure relies on a pretrained network that learned to classify images that are adequately registered to a template from others for which the registration failed. It reproduces the quality check procedure performed in [Wen et al., 2020]. It is an adaptation of [Fonov et al., 2018], using their pretrained models. Their original code can be found on GitHub.

Warning

This quality check procedure is specific to the t1-linear pipeline and should not be applied to other preprocessing procedures as the results may not be reliable. Moreover, you should be aware that this procedure may not be well adapted to de-identified data (for example images from OASIS-1) where parts of the images were removed (e.g. the face) or modified to guarantee anonymization.

Prerequisites

You need to execute the clinica run t1-linear and clinicadl extract pipelines prior to running this task.

Running the task

The task can be run with the following command line:

clinicadl quality-check t1-linear CAPS_DIRECTORY OUTPUT_TSV
where:

  • CAPS_DIRECTORY (Path) is the folder containing the results of the t1-linear pipeline and the output of the present command, both in a CAPS hierarchy.
  • OUTPUT_TSV (str) is the path to the output TSV file (filename included).

Options:

  • --subjects_sessions_tsv (Path) is the path to a TSV file containing the subjects/sessions list to check (filename included). Default will process all sessions available in caps_directory.
  • --threshold (float) is the threshold applied to the output probability when deciding if the image passed or failed. Default value: 0.5.
  • --batch_size (int) is the size of the batch used in the DataLoader. Default value: 1.
  • --n_proc (int) is the number of workers used by the DataLoader. Default value: 2.
  • --gpu/--no-gpu (bool) Use GPU for computing optimization. Default behaviour is to try to use a GPU and to raise an error if it is not found.

Outputs

The output of the quality check is a TSV file in which all the sessions (identified with their participant_id and session_id) are associated with a pass_probability value and a True/False pass value depending on the chosen threshold. An example of TSV file is:

participant_id session_id pass_probability pass
sub-CLNC01 ses-M00 0.9936990737915039 True
sub-CLNC02 ses-M00 0.9772214889526367 True
sub-CLNC03 ses-M00 0.7292165160179138 True
sub-CLNC04 ses-M00 0.1549495905637741 False
... ... ... ...

quality-check t1-volume - Evaluate t1-volume registration and gray matter segmentation

The quality check procedure is based on thresholds on different statistics that were empirically linked to images of bad quality. Three steps are performed to remove images with the following characteristics:

  1. a maximum value below 0.95,
  2. a percentage of non-zero values below 15% or higher than 50%,
  3. a similarity with the DARTEL template around the frontal lobe below 0.40. The similarity corresponds to the normalized mutual information. This allows checking that the eyes are not included in the brain volume.

Warning

This quality check procedure is specific to the t1-volume pipeline and should not be applied to other preprocessing procedures as the results may not be reliable.

Prerequisites

You need to execute the clinica run t1-volume pipeline prior to running this task.

Running the task

The task can be run with the following command line:

clinicadl preprocessing quality-check t1-volume CAPS_DIRECTORY OUTPUT_TSV GROUP_LABEL
where:

  • CAPS_DIRECTORY (Path) is the folder containing the results of the t1-volume pipeline and the output of the present command, both in a CAPS hierarchy.
  • OUTPUT_TSV (Path) is the path to an output directory in which TSV files will be created.
  • GROUP_LABEL (str) is the identifier for the group of subjects used to create the DARTEL template. You can check which groups are available in the groups/ folder of your caps_directory.

Outputs

This pipeline outputs 4 files:

  • QC_metrics.tsv containing the three QC metrics for all the images,
  • pass_step-1.tsv including only the images which passed the first step,
  • pass_step-2.tsv including only the images which passed the two first steps,
  • pass_step-3.tsv including only the images which passed all the three steps.

Manual quality check

This quality check is really conservative and may keep some images that are not of good quality. You may want to check the last images kept at each step to assess if their quality is good enough for your application.