imaging-study-design

Reasoning skill for medical imaging study design and biomarker selection. Use when the user asks to plan an imaging study, choose a preprocessing strategy, select an imaging biomarker, design a radiomics pipeline, handle DICOM de-identification, plan longitudinal imaging analysis, or pick a registration target. Triggers include "imaging study", "preprocessing strategy", "DICOM de-identification", "imaging biomarker", "radiomics", "longitudinal imaging", "registration target", "MNI vs native space", "scanner harmonization", "ComBat", "IBSI", "multi-site imaging", "burned-in PHI", "test-retest reliability", "volumetric biomarker", "diffusion MRI", "perfusion imaging", "fMRI study design", "spectroscopy biomarker".

仓库: awslabs/hcls-agent-skills
最近来源活动: 2026年6月9日 21:16
检测到的 SKILL.md 语言: 英语
星标: 16
分支: 6

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Downstream goal	Preprocessing priorities	What to avoid
Group comparison (cross-sectional)	Register to common template (MNI/SPM), smooth, modulate if VBM	Native-space-only analysis
Longitudinal atrophy / change	Rigid register follow-ups to subject baseline, then (optionally) baseline to template	Registering each timepoint independently to MNI
Lesion segmentation	Bias field correction, skull strip, intensity normalization; registration often optional	Aggressive smoothing that blurs lesion edges
Radiomics / texture	Standardize acquisition first (voxel size, reconstruction, binning); resample to isotropic; discretize intensity deterministically	Mixing protocols without harmonization; default-bin radiomics on heterogeneous data
Functional MRI	Slice-timing (if long TR), motion correction, distortion correction, coregister to anatomical, normalize if group, bandpass filter as appropriate	Normalizing before motion scrubbing; smoothing before ICA denoising
Diffusion MRI	Denoise, Gibbs unringing, eddy + motion correction, bias correction, then tensor/ODF fitting	Reordering these steps; skipping distortion correction when reverse-PE is available

Indication	Modality / contrast	Biomarker	Rationale
Alzheimer's disease, MCI	Structural T1 MRI	Hippocampal volume, cortical thickness, ventricular volume	Measures neurodegeneration directly
Multiple sclerosis	T2/FLAIR + T1	Lesion load, lesion count, brain parenchymal fraction, cord area	Disease burden and atrophy
White matter disease, DAI, schizophrenia	Diffusion MRI	FA, MD, RD, AD (tract-based or voxelwise)	Microstructural integrity
Acute stroke	DWI + PWI	DWI lesion volume, PWI-DWI mismatch, CBF	Core vs penumbra triage
Brain tumor (glioma)	DSC/DCE perfusion, MRS, diffusion	rCBV, Ktrans, ADC, NAA/Cho ratio	Grade, treatment response, pseudoprogression vs recurrence
Presurgical planning (epilepsy, tumor)	Task and resting-state fMRI, DTI tractography	Language/motor activation maps, tract-to-lesion distance	Functional localization
Neuronal integrity (various)	1H-MRS	NAA/Cr, Cho/Cr, mI/Cr	Metabolic markers of neuronal loss, membrane turnover
Oncology outside brain	CT, PET/CT, mpMRI	Volumetry, SUVmax/SUVpeak, radiomic signatures, ADC	Staging, response (RECIST, PERCIST)
Cardiac	Cine, LGE, T1/T2 mapping	EF, strain, LGE burden, native T1, ECV	Function, fibrosis, edema

Wrong: Registering each longitudinal timepoint independently to MNI Right: Build a within-subject template or rigidly register follow-ups to baseline first; only then normalize to MNI if needed Why: Subject-specific atrophy is absorbed into the warp field and the longitudinal change signal is lost
Wrong: Relying solely on tag-based DICOM de-identification Right: Include pixel inspection, private-tag policy, SR/PDF handling, UID remapping, and defacing in the de-identification plan Why: Burned-in PHI in pixels, private tags, SR/PDF narratives, and date-encoded UIDs all survive a naive tag-level scrub
Wrong: Building a radiomics model using features with test-retest ICC < 0.75 Right: Filter features by ICC ≥ 0.75 (preferably ≥ 0.9) and by segmentation robustness before any supervised selection Why: Unstable features poison reproducibility — signatures built on them will not generalize to external cohorts
Wrong: Ignoring scanner/site effects in multi-site imaging studies Right: Standardize protocols, apply ComBat or mixed-effects modeling, include site as a covariate, and pre-register the analysis plan Why: Site confounding can fully explain apparent biological effects, producing false-positive findings
Wrong: Fitting dozens of radiomics features to a dataset with few outcome events Right: Enforce EPV ≥ 10 (events per variable), use nested cross-validation, and report external validation Why: Over-fitting produces signatures that appear strong internally but fail completely on any external cohort
Wrong: Choosing an imaging biomarker by convenience rather than biological relevance Right: Run the Decision Framework (question → modality → biomarker) — the biomarker should be mechanistically linked to the clinical question Why: Using whole-brain volume when the question is hippocampal atrophy, or ADC when the question is perfusion, measures the wrong thing
Wrong: Deciding QC exclusion thresholds after seeing the analysis results Right: Pre-specify QC metrics and exclusion thresholds before analysis; report inclusion/exclusion counts transparently Why: Post-hoc threshold selection biases the study toward desired results
Wrong: Smoothing or interpolating through a lesion or tumor ROI Right: Keep ROI-based measurements in native space, or use lesion-filling / cost-function masking before template normalization Why: Smoothing across lesion boundaries corrupts texture features and blurs lesion edges, invalidating radiomics and lesion-aware analyses