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asl-skill

Quantify cerebral blood flow from ASL perfusion MRI using the Buxton model.

89|5|Updated Mar 25, 2026
Search Tags:#neuroimaging#nifti#fsl#asl#perfusion mri#cbf quantification#pcasl
One-click install
npx skills add https://github.com/CUHK-AIM-Group/NeuroDiscovery --skill asl-skill-cuhk-aim-group
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: asl-skill
Source: https://github.com/CUHK-AIM-Group/NeuroDiscovery/tree/main/skills/asl-skill
Command: npx skills add https://github.com/CUHK-AIM-Group/NeuroDiscovery --skill asl-skill-cuhk-aim-group

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, nibabel, and includes scripts (resource) components.

What problem does it solve? Processing Arterial Spin Labeling (ASL) perfusion MRI data requires coordinating motion correction, M0 normalization, and CBF quantification across multiple neuroimaging tools, which is error-prone when done manually. This Skill orchestrates the full ASL workflow and computes absolute CBF maps in mL/100g/min from pCASL, CASL, or PASL data. ## Core Features & Use Cases - CBF Quantification: Computes absolute CBF maps from ASL difference images and M0 references using the Buxton single-compartment model, with strategy-specific defaults for pCASL, CASL, and PASL. - Preprocessing Delegation: Coordinates motion correction, T1w coregistration, partial volume correction, and MNI normalization through FSL-based tool skills. - ROI-Based Analysis: Extracts mean CBF values per atlas region into CSV summaries for group-level statistical analysis. - Use Case: A researcher with pCASL data (PLD 1.8 s, label duration 1.8 s, 3T) needs a CBF map and per-region values; the Skill preprocesses the ASL series, quantifies CBF, and outputs a NIfTI map plus ROI CSV. ## Quick Start Process my pCASL data with M0 reference to generate a CBF map and ROI summary using the ASL skill.

Frequently Asked Questions about asl-skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute CBF from ASL MRI data?▼

Compute CBF by dividing the ASL difference image (control minus label) by the M0 reference and applying the Buxton model with your labeling efficiency, label duration, and post-labeling delay. The compute_cbf.py script outputs a CBF map in mL/100g/min as NIfTI.

What is the difference between pCASL, CASL, and PASL quantification?▼

pCASL and CASL use the Buxton model with label duration and post-labeling delay, while PASL uses QUIPSS II timing parameters TI1 and TI2. Labeling efficiency defaults differ: 0.85 for pCASL, 0.95 for CASL, and 0.98 for PASL.

Can I quantify CBF without an M0 reference image?▼

Absolute CBF quantification requires an M0 image for normalization. Without M0, only relative CBF can be computed using the --no-m0 flag, which is not comparable across subjects or sessions.

What T1 of blood value should I use for ASL quantification?▼

T1 of arterial blood depends on field strength: approximately 1.35 s at 1.5T, 1.65 s at 3T, and 2.1 s at 7T. The script selects this automatically from the --field-strength argument.

Why is partial volume correction important for ASL CBF maps?▼

ASL has low spatial resolution of about 3-4 mm, so voxels mix gray matter, white matter, and CSF signals. Partial volume correction using tissue segmentation from T1w data prevents underestimation of gray matter CBF.

What are the limitations of ASL perfusion imaging?▼

ASL has inherently low SNR compared to BOLD fMRI, so averaging multiple control-label pairs is recommended. Incorrect PLD or label duration values invalidate CBF quantification, and the method is for research use only, not clinical decision-making.

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