GE ASL Self-Contained Processing (GEASLscp)

GE ASL

**Content creator:** Katie Jobson

**Pipeline developer:** Manuel Taso (Penn Medicine)

This Docker container processes GE Arterial Spin Labeling (ASL) MRI data without requiring a structural scan.

Description

This pipeline processes GE 3D pCASL data and computes cerebral blood flow (CBF) maps. A single input file is required containing both the ASL timeseries data and M0 calibration image.

Processing Steps

  1. DICOM to NIfTI conversion using dcm2niix
  2. Automatic ASL/M0 detection based on signal intensity
  3. Skull stripping using FreeSurfer’s mri_synthstrip
  4. CBF quantification using the standard kinetic model (Alsop 2015)
  5. Registration to template space using ANTs
  6. ROI-based analysis with brain atlases for Alzheimer’s specific regions
  7. PDF report generation with QC images and regional CBF values

Disclaimer: This CBF quantification does not replicate the automatic CBF calculation performed on the GE scanner console. Results may differ from scanner-generated CBF maps.

Inputs

Input Description
dicom_nifti_asl DICOM zip containing ASL and M0, or ASL NIfTI file
nifti_m0 M0 NIfTI file (required when using NIfTI input)

Configuration Parameters

Parameter Description Flag
ld Labeling duration (seconds) -l
pld Post-labeling delay (seconds) -p
avg Number of averages -n
skip_extended Skip registration and PDF generation -e

For NIfTI input, parameters must always be provided manually.

Outputs

Output Description
{subject_id}_cbf.nii.gz Quantitative CBF map
{subject_id}_output.pdf PDF report with QC images and regional CBF tables
{subject_id}_qc.pdf Quality control PDF
stats/ Directory containing regional CBF text files

Docker Usage

Basic Usage:

docker run -v /path/to/input:/flywheel/v0/input \
           -v /path/to/output:/flywheel/v0/output \
           kjobson/geaslscp:latest \
           -a /flywheel/v0/input/asl_dicom.zip

With Manual Parameters:

docker run -v /path/to/input:/flywheel/v0/input \
           -v /path/to/output:/flywheel/v0/output \
           kjobson/geaslscp:latest \
           -a /flywheel/v0/input/asl_dicom.zip \
           -l 3 \
           -p 2.025 \
           -n 2

Command Line Options

Option Description
-a Path to ASL DICOM zip or NIfTI file
-m Path to M0 NIfTI file (required for NIfTI input)
-l Labeling duration (seconds)
-p Post-labeling delay (seconds)
-n Number of averages
-e Skip extended analysis
-s Subject ID

Flywheel Deployment

  1. Install the Flywheel CLI: https://docs.flywheel.io/CLI/
  2. Log in to your Flywheel instance: fw-beta login your-api-key
  3. Build and upload:
fw-beta gear build .
fw-beta gear upload .

Software Dependencies

  • FreeSurfer 7.4.1 - skull stripping (mri_synthstrip)
  • FSL 6.0.7.1 - image math, registration tools
  • ANTs 2.5.4 - nonlinear registration
  • dcm2niix - DICOM to NIfTI conversion
  • Python 3 with: scipy, nibabel, matplotlib, nilearn, reportlab

Other Resources

Citation

If you use this pipeline, please cite the relevant software packages and acknowledge the developers.

Reference: Alsop DC, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications. Magn Reson Med. 2015;73(1):102-116.

License

MIT License