asl-skill

name	asl-skill
description	Use this skill whenever the user wants to process Arterial Spin Labeling (ASL) perfusion MRI data including CBF (cerebral blood flow) quantification, ASL preprocessing (motion correction, partial volume correction, M0 normalization), or ASL-based brain perfusion analysis. Triggers include: 'ASL', 'ASL processing', 'CBF', 'cerebral blood flow', 'perfusion MRI', 'arterial spin labeling', 'pCASL', 'CASL', 'PASL', or any request involving ASL perfusion data.
license	MIT License (NeuroClaw custom skill – freely modifiable within the project)
layer	subagent
skill_type	modality
dependencies	["fsl-tool","nibabel-skill","claw-shell"]
complementary_skills	["smri-skill","fmri-skill"]

ASL Skill (Modality Layer)

Overview

asl-skill is the NeuroClaw modality-layer interface skill responsible for all Arterial Spin Labeling (ASL) perfusion MRI data processing tasks.

It strictly follows the NeuroClaw hierarchical design principles:

• This skill only describes WHAT needs to be done and which tool skill to delegate to.
• It contains no implementation code or concrete commands.
• All concrete execution is delegated to existing base/tool skills: fsl-tool, nibabel-skill, and claw-shell.
• Companion scripts in scripts/ provide reference implementations for CBF quantification.

Core workflow (never bypassed):

1. Identify input ASL data and labeling strategy (pCASL, CASL, or PASL).
2. Ensure T1w structural data is available (via smri-skill if not yet processed).
3. Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
4. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
5. On confirmation, delegate every step to the appropriate skill via claw-shell.
6. After execution, save all outputs in a clean directory structure (asl_output/).

Research use only.

---

Quick Reference (Common ASL Tasks)

Task	What needs to be done	Delegate to which tool skill	Expected output
ASL preprocessing	Motion correction, masking, registration to T1w	fsl-tool (ASL_PREPCORE)	Preprocessed ASL in T1w space
M0 normalization	Divide ASL difference image by M0 reference image to get perfusion signal	fsl-tool or scripts/compute_cbf.py	Normalized perfusion map
CBF quantification	Convert perfusion signal to absolute CBF (mL/100g/min) using Buxton model	scripts/compute_cbf.py	CBF map (NIfTI) + ROI summary (CSV)
Partial volume correction	Correct CBF for gray/white matter partial volume effects	fsl-tool + tissue segmentation	PVC-corrected CBF map
ASL-to-MNI normalization	Warp CBF map to MNI152 template for group analysis	fsl-tool (FNIRT) or smri-skill	CBF in MNI152 space
ROI-based CBF extraction	Extract mean CBF from atlas-defined ROIs	fsl-tool + atlas	Per-region CBF values (CSV)
Quality control	Check for outliers, low SNR, motion artifacts in ASL series	scripts/compute_cbf.py (--qc)	QC report

---

ASL Labeling Strategies

Strategy	Description	Typical Parameters
pCASL (pseudo-Continuous ASL)	Most common; single PLD, good SNR	Label duration: 1.5–2.0 s, PLD: 1.5–2.0 s
CASL (Continuous ASL)	Longer labeling, higher SNR but more sensitive to transit effects	Label duration: 2–4 s, PLD: 1–2 s
PASL (Pulsed ASL)	Short labeling, lower SNR, no separate M0 needed (QUIPSS II)	Bolus thickness: 10–15 cm, TI1/TI2: 700/1800 ms

---

Core CBF Quantification Model

The Buxton single-compartment model for pCASL:

CBF = (6000 * ΔM * λ) / (2 * α * M0 * T1b * (exp(-w/T1b) - exp(-(τ+w)/T1b)))   [mL/100g/min]

Where:

• ΔM = ASL difference image (control - label)
• M0 = equilibrium magnetization of arterial blood
• λ = blood-tissue water partition coefficient (0.9 mL/g)
• α = labeling efficiency (0.85 for pCASL, 0.95 for CASL, 0.98 for PASL)
• T1b = T1 of arterial blood at 3T (~1.65 s) or 1.5T (~1.35 s)
• w = post-labeling delay (PLD)
• τ = label duration

---

Scripts

scripts/compute_cbf.py

Computes CBF maps from ASL difference images and M0 reference.

python skills/asl-skill/scripts/compute_cbf.py \
  --diff /path/to/asl_diff.nii.gz \
  --m0 /path/to/m0_reference.nii.gz \
  --output /path/to/asl_output/cbf_map.nii.gz \
  --roi-summary /path/to/asl_output/cbf_roi.csv \
  --roi-atlas /path/to/atlas_in_asl_space.nii.gz \
  --label-strategy pcasl \
  --pld 1.8 \
  --label-duration 1.8 \
  --field-strength 3.0

---

Standard Output Layout

asl_output/
├── preprocessed/          # Motion-corrected, registered ASL
├── cbf/                   # CBF maps
│   ├── cbf_map.nii.gz
│   ├── cbf_roi.csv
│   └── cbf_mni.nii.gz    # (if normalization requested)
├── pvc/                   # Partial volume corrected CBF (if requested)
├── qc/                    # Quality control reports
│   └── asl_qc_report.csv
└── logs/

---

Installation (Handled by dependency-planner)

No manual installation required at this layer. When first used, asl-skill automatically calls dependency-planner to ensure fsl-tool, nibabel-skill, and claw-shell are ready.

---

Important Notes & Limitations

• ASL has inherently low SNR compared to BOLD fMRI; averaging multiple control-label pairs is recommended.
• M0 image is required for absolute CBF quantification; if absent, only relative CBF can be computed.
• PLD and labeling duration must be known from the acquisition protocol; incorrect values invalidate CBF.
• At 3T, T1b ≈ 1.65 s; at 1.5T, T1b ≈ 1.35 s.
• Partial volume correction is important for ASL due to its low resolution (~3–4 mm).
• ASLPrep (https://aslprep.readthedocs.io/) is the recommended automated pipeline for large cohorts.
• This skill is for research workflows; not for clinical decision-making.

---

When to Call This Skill

• After smri-skill when T1w structural preprocessing is complete and ASL data needs processing.
• When the user needs CBF quantification from pCASL, CASL, or PASL data.
• When ASL-to-T1w coregistration or normalization to MNI space is required.
• When partial volume correction is requested for ASL perfusion analysis.
• When dataset skills (e.g., PNC) delegate ASL processing.

---

Complementary / Related Skills

• smri-skill → T1w structural preprocessing (brain extraction, tissue segmentation for PVC)
• fmri-skill → if ASL is used alongside BOLD for multimodal analysis
• fsl-tool → ASL_PREPCORE (preprocessing), FLIRT/FNIRT (registration/normalization), BASIL (CBF quantification)
• nibabel-skill → NIfTI I/O for mask manipulation
• nilearn-tool → ROI-based CBF extraction
• brain-visualization → CBF map visualization

---

Reference

• Alsop et al. (2015): Recommended implementation of ASL (Magnetic Resonance in Medicine)
• Buxton et al. (1998): General kinetic model for ASL (Journal of Cerebral Blood Flow & Metabolism)
• ASLPrep: https://aslprep.readthedocs.io/
• FSL BASIL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BASIL
• BIDS ASL extension: https://bids-specification.readthedocs.io/en/stable/04-modality-specific-files/11-arterial-spin-labeling.html

Created At: 2026-05-06 12:19 HKT Last Updated At: 2026-05-06 12:19 HKT Author: chengwang96