CT Perfusion Skill

Research workflows for NeuroDiscovery. Read the input and method contract before processing data. These reference implementations are not clinically validated diagnostic software; do not label an ischemic core, penumbra, treatment eligibility or patient outcome from their maps.

Choose the input route

• 4D CT: accept a NIfTI HU series and acquisition timing, reviewed precontrast frames, brain mask and arterial input function (AIF) mask. For DICOM, first use a validated converter with rescale slope/intercept and per-frame acquisition timing preserved; never guess slice ordering or time units. Shuttle/per-slice asynchronous data need explicit upstream temporal reconstruction.
• Unaligned frames: use scripts/motion_correct.py, inspect motion and all-frame coverage, then save a reviewed metadata file. Do not declare registration good just because the optimizer stopped. Register/select all masks in the fixed frame.
• Already derived maps: use scripts/ctp.py summarize. Require units and a common affine/grid; never infer vendor scaling or replace missing-value codes with meaningful zero perfusion.

Dependencies: numpy scipy pandas nibabel matplotlib; motion correction additionally needs SimpleITK. They are available through the project's clinical-outputs optional dependency group.

python skills/ctp-skill/scripts/motion_correct.py --series ctp.nii.gz --metadata acquisition.json --output-dir ctp_motion

python skills/ctp-skill/scripts/ctp.py compute --series aligned_ctp.nii.gz --metadata reviewed_acquisition.json --mask brain.nii.gz --aif-mask aif.nii.gz --atlas atlas_in_ct_space.nii.gz --output-dir ctp_maps

python skills/ctp-skill/scripts/ctp.py summarize --cbf cbf.nii.gz --cbv cbv.nii.gz --mtt mtt.nii.gz --tmax tmax.nii.gz --metadata map_units.json --mask brain.nii.gz --output-dir ctp_summary

For irregular acquisition times, explicitly choose --resample-dt (seconds) after reviewing sampling; the script otherwise rejects them. Optional --vof-mask applies venous area-based AIF scaling. Record and justify --svd-cutoff, --density, and any --hematocrit-factor; defaults are disclosed research assumptions, not scanner calibration. No automatic AIF selection is performed.

Deliver and check

Return CBF.nii.gz, CBV.nii.gz, MTT.nii.gz, Tmax.nii.gz, analysis_mask.nii.gz, roi_summary.csv, maps_qc.png, qc.json and run_manifest.json; time-series processing also returns curves.csv and curves_qc.png. Preserve NaNs for invalid/outside-mask voxels and report valid counts. Review bolus truncation, residuals, negative Tmax and mask coverage before downstream analysis. Plots are labeled voxel-index slices, not a clinical radiological viewer.

python -m models.common.research_outputs ctp_maps/run_manifest.json --require CBF CBV MTT Tmax roi_summary map_figure qc

For cohort modeling, join map/ROI features to pseudonymous subject IDs with an explicit atlas and feature schema. Route patient grouping to subject-subtyping and trained-model explanations to model-interpretability; processing one scan does not authorize a cohort experiment or external upload.

Created At: 2026-09-14 14:38:01 HKT Last Updated At: 2026-09-14 15:05:31.197 HKT Author: chengwang96