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radiology-preprocessing

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Updated 10-15 18:46

About this skill

Radiology Preprocessing

Overview

Deterministic commands for preprocessing structural brain MRI (T1w, T2w, FLAIR) and related volumetric radiology data. Covers the canonical pipeline used by most downstream analyses (segmentation, registration studies, deep-learning training): reorient → bias correction → skull strip → registration → intensity normalization.

Tools used:

  • HD-BET — CNN-based brain extraction (state-of-the-art on pathological brains).
  • FSL — bet, flirt, fnirt, fslmaths, fslreorient2std.
  • ANTs — N4BiasFieldCorrection, antsRegistrationSyNQuick.sh, antsApplyTransforms.
  • FreeSurfer — mri_convert for resampling/format conversion.

All commands operate on NIfTI (.nii / .nii.gz). Run in a POSIX shell with the respective tool on PATH.

Usage

Standard T1w preprocessing pipeline

Run these steps in order on a single subject volume:

# 1. Reorient to standard (MNI-style RAS orientation)
fslreorient2std input.nii.gz reoriented.nii.gz

# 2. N4 bias field correction (BEFORE skull stripping)
N4BiasFieldCorrection -d 3 \
  -i reoriented.nii.gz \
  -o [n4.nii.gz,bias_field.nii.gz] \
  -s 3 \
  -c [50x50x30x20,1e-6] \
  -b [300]

# 3. Skull strip with HD-BET
hd-bet -i n4.nii.gz -o brain.nii.gz -device 0 -mode fast -tta 0
# CPU fallback (slower, no CUDA required): -device cpu

# 4. Register to MNI152 template (SyN nonlinear)
antsRegistrationSyNQuick.sh -d 3 \
  -f $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz \
  -m brain.nii.gz \
  -o sub2mni_ \
  -t s

# 5. Z-score intensity normalization within brain mask
fslmaths brain.nii.gz -mas brain_mask.nii.gz brain_masked.nii.gz
MEAN=$(fslstats brain_masked.nii.gz -k brain_mask.nii.gz -M)
STD=$(fslstats brain_masked.nii.gz -k brain_mask.nii.gz -S)
fslmaths brain_masked.nii.gz -sub $MEAN -div $STD -mas brain_mask.nii.gz brain_zscore.nii.gz

HD-BET writes brain.nii.gz and brain_mask.nii.gz (mask has _mask suffix by default).

GPU acceleration

HD-BET defaults to GPU; use -device 0 for first CUDA device. -mode fast -tta 0 disables test-time augmentation for ~5x speedup with minimal quality loss.

hd-bet -i n4.nii.gz -o brain.nii.gz -device 0

Response Format

  • Lead with the command or code the user needs — explain after
  • Structure as: confirm inputs → working code → key parameters explained → gotchas
  • One complete working example per task; do not show every alternative
  • Keep code comments minimal and functional (what, not why-it-exists)
  • Target: 50-100 lines of code with brief surrounding explanation

Do not narrate the pipeline ordering rationale or preprocessing theory unless explicitly asked — produce the correct commands in sequence with brief parameter justification.

Core Concepts

1. Skull stripping (brain extraction)

HD-BET (preferred — robust to pathology):

hd-bet -i input.nii.gz -o output_bet.nii.gz -device cpu -mode fast -tta 0

FSL BET (fallback, classical):

bet input.nii.gz output_bet.nii.gz -R -f 0.3 -g 0
  • -R: robust centre-of-gravity estimation.
  • -f 0.3: fractional intensity threshold (lower = larger brain mask).
  • -g 0: vertical gradient in threshold.

2. Bias field correction — N4 (ANTs)

Correct low-frequency intensity inhomogeneity from RF coil / B1 field. Always run before skull stripping — the bias field estimate is better with the skull in place, and a skull-stripped input produces artifacts near the brain boundary.

N4BiasFieldCorrection -d 3 \
  -i input.nii.gz \
  -o [corrected.nii.gz,bias_field.nii.gz] \
  -s 3 \
  -c [50x50x30x20,1e-6] \
  -b [300]
  • -d 3: 3D image.
  • -s 3: shrink factor (speedup).
  • -c [50x50x30x20,1e-6]: max iterations per level + convergence threshold.
  • -b [300]: B-spline mesh resolution (mm).

3. Registration — ANTs

antsRegistrationSyNQuick.sh is the sensible default wrapper.

# Rigid (6 DOF) — same subject, different timepoint/modality
antsRegistrationSyNQuick.sh -d 3 -f fixed.nii.gz -m moving.nii.gz -o output_ -t r

# Affine (12 DOF) — cross-subject coarse alignment
antsRegistrationSyNQuick.sh -d 3 -f fixed.nii.gz -m moving.nii.gz -o output_ -t a

# SyN (nonlinear) — atlas / MNI registration
antsRegistrationSyNQuick.sh -d 3 -f fixed.nii.gz -m moving.nii.gz -o output_ -t s

Outputs (for -t s):

  • output_0GenericAffine.mat — affine transform.
  • output_1Warp.nii.gz / output_1InverseWarp.nii.gz — displacement fields.
  • output_Warped.nii.gz — moving resampled into fixed space.

Apply the saved transform to another image (e.g., a segmentation):

# For intensity images — Linear interpolation
antsApplyTransforms -d 3 \
  -i input.nii.gz \
  -r reference.nii.gz \
  -o output.nii.gz \
  -t output_1Warp.nii.gz \
  -t output_0GenericAffine.mat

# For label maps — NearestNeighbor interpolation
antsApplyTransforms -d 3 \
  -i labels.nii.gz \
  -r reference.nii.gz \
  -o labels_in_ref.nii.gz \
  -n NearestNeighbor \
  -t output_1Warp.nii.gz \
  -t output_0GenericAffine.mat

ANTs applies transforms in reverse order: the last -t is applied first. For moving→fixed, the affine is applied before the warp, so the CLI order is -t warp -t affine.

4. Registration — FSL FLIRT + FNIRT

Linear (affine):

flirt -in moving.nii.gz -ref fixed.nii.gz \
  -out output.nii.gz -omat affine.mat -dof 12

Nonlinear (requires affine init):

fnirt --in=moving.nii.gz \
  --ref=$FSLDIR/data/standard/MNI152_T1_1mm.nii.gz \
  --aff=affine.mat \
  --cout=warp \
  --iout=output.nii.gz

Apply warp to another image:

applywarp -i input.nii.gz -r MNI152_T1_1mm.nii.gz -w warp -o output.nii.gz
# For labels:
applywarp -i labels.nii.gz -r MNI152_T1_1mm.nii.gz -w warp -o labels_mni.nii.gz --interp=nn

5. Intensity normalization

Needed before most ML models — raw MRI intensities are arbitrary units and vary across scanners/sessions.

Z-score within brain mask (most common):

MEAN=$(fslstats brain.nii.gz -k brain_mask.nii.gz -M)
STD=$(fslstats brain.nii.gz -k brain_mask.nii.gz -S)
fslmaths brain.nii.gz -sub $MEAN -div $STD -mas brain_mask.nii.gz zscore.nii.gz

White matter normalization (scanner-invariant):

  1. Segment WM with FSL FAST or FreeSurfer.
  2. Compute mean intensity inside WM mask: fslstats brain.nii.gz -k wm_mask.nii.gz -M.
  3. Divide volume by that mean.

Histogram matching — match the intensity histogram to a reference subject:

  • ANTs: ImageMath 3 out.nii.gz HistogramMatch moving.nii.gz reference.nii.gz.
  • SimpleITK: sitk.HistogramMatchingImageFilter().

6. Resampling

FreeSurfer (preferred for isotropic resampling):

mri_convert --voxel-size 1 1 1 input.nii.gz output_1mm.nii.gz

FSL:

flirt -in input.nii.gz -ref input.nii.gz -applyisoxfm 1 -out output_1mm.nii.gz
# For labels use: -interp nearestneighbour

Standard pipeline order

reorient (fslreorient2std)
  → N4 bias correction
  → skull strip (HD-BET)
  → registration (ANTs SyN or FSL FNIRT)
  → intensity normalization (z-score / WM)

Resampling to isotropic 1 mm is usually done either at reorient time (if input is anisotropic) or implicitly by registration to a 1 mm template.

Quick Reference

Task Command
Reorient to standard fslreorient2std in.nii.gz out.nii.gz
N4 bias correction N4BiasFieldCorrection -d 3 -i in.nii.gz -o [out.nii.gz,bias.nii.gz] -s 3 -c [50x50x30x20,1e-6] -b [300]
Skull strip (HD-BET) hd-bet -i in.nii.gz -o brain.nii.gz -device cpu -mode fast -tta 0
Skull strip (BET) bet in.nii.gz brain.nii.gz -R -f 0.3 -g 0
Rigid register (ANTs) antsRegistrationSyNQuick.sh -d 3 -f fix.nii.gz -m mov.nii.gz -o out_ -t r
Affine register (ANTs) antsRegistrationSyNQuick.sh -d 3 -f fix.nii.gz -m mov.nii.gz -o out_ -t a
SyN register (ANTs) antsRegistrationSyNQuick.sh -d 3 -f fix.nii.gz -m mov.nii.gz -o out_ -t s
Apply ANTs transform (image) antsApplyTransforms -d 3 -i in.nii.gz -r ref.nii.gz -o out.nii.gz -t out_1Warp.nii.gz -t out_0GenericAffine.mat
Apply ANTs transform (labels) Add -n NearestNeighbor
FLIRT affine flirt -in mov.nii.gz -ref fix.nii.gz -out out.nii.gz -omat aff.mat -dof 12
FNIRT nonlinear fnirt --in=mov.nii.gz --ref=MNI152_T1_1mm.nii.gz --aff=aff.mat --cout=warp --iout=out.nii.gz
Z-score normalize fslmaths in.nii.gz -sub $MEAN -div $STD -mas mask.nii.gz out.nii.gz
Histogram match (ANTs) ImageMath 3 out.nii.gz HistogramMatch mov.nii.gz ref.nii.gz
Resample isotropic 1 mm mri_convert --voxel-size 1 1 1 in.nii.gz out.nii.gz

Standard templates (FSL)

  • $FSLDIR/data/standard/MNI152_T1_1mm.nii.gz — whole-head.
  • $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz — brain-only.
  • $FSLDIR/data/standard/MNI152_T1_2mm_brain.nii.gz — 2 mm for faster registration.

Common Mistakes

  • Wrong: Running skull stripping before bias field correction Right: Always run N4 bias correction first, then skull strip Why: Bias field estimation needs the full field of view including skull/neck to fit a smooth B-spline; N4 on an already-stripped brain introduces edge artifacts and leaves residual inhomogeneity

  • Wrong: Using linear/trilinear interpolation when resampling label maps Right: Use NearestNeighbor (ANTs: -n NearestNeighbor; FSL: --interp=nn) for any discrete label volume Why: Linear interpolation on integer segmentation labels produces meaningless fractional values and destroys class boundaries

  • Wrong: Specifying ANTs transforms in forward CLI order (affine then warp) Right: Use -t output_1Warp.nii.gz -t output_0GenericAffine.mat — warp first in CLI, affine second Why: antsApplyTransforms composes in reverse CLI order (last -t applied first); reversing them silently produces misregistered output

  • Wrong: Registering each longitudinal timepoint independently to MNI Right: Build a subject-specific midpoint template, register each timepoint to it, then register the midpoint once to MNI Why: Independent registration introduces asymmetric interpolation bias that corrupts longitudinal measurements (e.g., atrophy)

  • Wrong: Skipping reorientation at the start of the pipeline Right: Always run fslreorient2std first on inputs from different scanners/vendors Why: Inconsistent storage orientations cause downstream tools to misinterpret coordinate frames

  • Wrong: Computing z-score normalization over the full volume including background Right: Compute mean/std within the brain mask only (fslstats -k brain_mask.nii.gz) Why: Including background zeros drags the mean down and inflates the standard deviation, producing incorrect normalization

References