Outputs of ASLPrep

ASLPrep generates three broad classes of outputs:

  1. Visual QA (quality assessment) reports: one HTML per subject, per session (if applicable), that allows the user to conduct a thorough visual assessment of the raw and processed data. This also includes quality control measures.
  2. Derivatives (preprocessed data and computed CBF): the input ASL data ready for analysis, (e.g., after the various preparation procedures have been applied), the brain mask, and ASL images after masking has been applied. Other outputs include computed CBF maps and post-processed data such as denoised and partial volume-corrected CBF.
  3. Confounds and quality controls: a confounds matrix that inlcudes framewise displacement, motion parameters, coregistration and registration quality indices, and cbf quality controls.

Visual Reports

ASLPrep outputs summary reports are written to <output dir>/aslprep/sub-<subject_label>.html. These reports provide a quick way to make visual inspection of the results easy. View a sample report.

Derivatives of ASLPrep

Preprocessed, or derivative, data are written to <output dir>/aslprep/sub-<subject_label>/. The BIDS Derivatives RC1 specification describes the naming and metadata conventions that we follow.

Anatomical derivatives

Anatomical derivatives are placed in each subject’s anat subfolder:: These derivatives are the same as smriprep output:


Spatially-standardized derivatives are denoted with a space label, such as MNI152NLin2009cAsym, while derivatives in the original T1w space omit the space- keyword.

Additionally, the following transforms are saved:


ASL and CBF derivatives

ASL derivatives are stored in the perf/ subfolder. All derivatives contain task-<task_label> (mandatory) and run-<run_index> (optional), and these will be indicated with [specifiers]:

    sub-<subject_label>_[specifiers]_space-<space_label>_aslref.nii.gz # asl reference image
    sub-<subject_label>_[specifiers]_space-<space_label>_desc-brain_mask.nii.gz # asl brain mask
    sub-<subject_label>_[specifiers]_space-<space_label>_desc-preproc_asl.nii.gz # preprocessed asl timeseries
    sub-<subject_label>_[specifiers]_space-<space_label>_cbf.nii.gz  # computed cbf timeseries
    sub-<subject_label>_[specifiers]_space-<space_label>_mean_cbf.nii.gz # mean cbf

SCORE and SCRUB Outputs:

sub-<subject_label>_[specifiers]_space-<space_label>_desc-score_cbf.nii.gz # cbf timeseries denoised with  SCORE
sub-<subject_label>_[specifiers]_space-<space_label>_desc-score_mean_cbf.nii.gz # mean cbf denoised with SCORE
sub-<subject_label>_[specifiers]_space-<space_label>_desc-scrub_cbf.nii.gz # mean CBF denoised with SCRUB

BASIL outputs:

sub-<subject_label>_[specifiers]_space-<space_label>_desc-basil_cbf.nii.gz # cbf computed with BASIL
sub-<subject_label>_[specifiers]_space-<space_label>_desc-pvc_cbf.nii.gz #  partial volume corrected cbf with BASIL
sub-<subject_label>_[specifiers]_space-<space_label>_desc-bat_cbf.nii.gz # bolus arrivsl time (in seconds)

Regularly gridded outputs (images): Volumetric output space labels (<space_label> above, and in the following) include T1w and MNI152NLin2009cAsym (default).

Extracted confounding time series: For each ASL run processed with ASLPrep, an accompanying confounds file will be generated. CBF Confounds are saved as a TSV file:


These TSV tables look like the example below, where each row of the file corresponds to one time point found in the corresponding ASL time series:

std_dvars  dvars   framewise_displacement  trans_x trans_y trans_z rot_x   rot_y   rot_z
n/a        n/a     n/a     0       0       0       -0.00017029     0       0
1.168398   17.575331       0.0721193       0       0.0207752       0.0463124       -0.000270924    0       0
1.085204   16.323904       0.0348966       0       0       0.0457372       0       0       0
1.01591    15.281561       0.0333937       0.010164        -0.0103568      0.0424513       0       0       0.00019174

CBF quality control

ASLPrep produces a quality control (QC) file for each ASL run:


The following QC measures were estimated: framewise displacement and relative root mean square (relRMS). Other QC measurers include dice and jaccard indices, cross-correlation, coverage estimates of the coregistration quality of ASL and T1w images, and normalization quality of ASL image to the template. The quality evaluation index (QEI) was also computed for CBF [Sudipto Dolui 2016]. The QEI is included for objective quality evaluation of CBF maps. It quantifies the quality of the CBF image based on structural similarity, spatial variability, and percentage of voxels in gray matter with negative CBF values.

CBF Confounds

Confounds include the six head-motion parameters (three rotations and three translations), which are common outputs from the head-motion correction (also known as realignment). ASLPrep also generates framewise displacement, DVARS and std_dvars. Confound variables calculated in ASLprep are stored separately for each subject, session and run in TSV files, with one column for each confound variable.