warpkit

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A Python library for neuroimaging transforms, focused on the Multi-Echo DIstortion Correction (MEDIC) algorithm. The paper is published in Imaging Neuroscience at https://doi.org/10.1162/IMAG.a.1262.

The phase-unwrapping core is a self-contained C++17 port of ROMEO.

Installation

From PyPI (recommended)

pip install warpkit

Pre-built wheels are published for Linux (x86_64 + aarch64) and macOS (universal2). If pip falls back to a source build and fails, please open an issue with the output of pip install warpkit -v.

Standalone binaries (no Python required)

Each GitHub release attaches a zip per arch (linux-x86_64, linux-aarch64, macos-arm64) containing all seven wk-* CLIs as standalone binaries — no Python install or system ITK needed. Extract, add to PATH, and run. See the bundled README.md inside the zip for install/PATH instructions and the macOS Gatekeeper note.

Docker

docker run -it --rm ghcr.io/vanandrew/warpkit:latest --help

The image's entrypoint is the wk-medic CLI.

From source

You need a C++17 compiler and CMake ≥ 3.24. Everything else (Python, build deps, ITK) is resolved by uv.

git clone https://github.com/vanandrew/warpkit.git
cd warpkit
uv sync --group dev --config-setting editable_mode=strict

editable_mode=strict is the recommended setuptools editable layout — it avoids the import-path pitfalls of the default editable install. Append -v to uv sync if a build fails and include the full log when reporting the issue.

What is MEDIC?

MEDIC takes ME-EPI phase data:

phase

and turns it into a per-frame field map:

field map

You can then use these field maps to distortion-correct your data.

Using MEDIC

warpkit is meant to be integrated into a larger neuroimaging pipeline. The Python entry point:

import nibabel as nib
from warpkit.distortion import medic
from warpkit.utilities import displacement_map_to_field

# each list entry is a different echo
phases = [nib.load(p) for p in phases_paths]
magnitudes = [nib.load(p) for p in magnitude_paths]
TEs = [TE1, TE2, ...]  # milliseconds
total_readout_time = ...  # seconds
phase_encoding_direction = ...  # one of i, j, k, i-, j-, k-, x, y, z, x-, y-, z-

field_maps_native, displacement_maps, field_maps = medic(
    phases, magnitudes, TEs, total_readout_time, phase_encoding_direction
)

Returns are nibabel.Nifti1Image objects:

  • field_maps_native — field maps in distorted space (Hz). Mostly useful for debugging.
  • displacement_maps — displacement in undistorted space (mm). Convert with displacement_map_to_field before applying.
  • field_maps — field maps in undistorted space (Hz). Equivalent to topup/fugue output, but framewise.

To convert a displacement map to a per-frame warp field for your tool of choice:

displacement_maps.to_filename("/path/to/save.nii.gz")

displacement_field = displacement_map_to_field(
    displacement_maps, axis="y", format="itk", frame=0
)

format selects the output convention. Apply with the matching tool:

format Apply with Notes
itk warpkit.utilities.resample_image warpkit's internal format.
ants antsApplyTransforms ANTs uses ITK underneath, but its warp convention differs from ours.
afni 3dNwarpApply
fsl applywarp If you'd rather use fugue, feed it field_maps instead.

CLI

All warpkit CLIs are installed on PATH with a wk- prefix to avoid colliding with same-named tools from FSL/ANTs/AFNI/etc.:

Command Purpose
wk-medic End-to-end MEDIC pipeline: phase + magnitude → field maps + displacement maps.
wk-unwrap-phase Stage 1: ROMEO multi-echo phase unwrapping → unwrapped phase + per-frame masks.
wk-compute-fieldmap Stage 2: take stage-1 outputs → native + displacement + undistorted-space field maps.
wk-apply-warp Resample an image through a displacement map / field (single or per-frame series).
wk-convert-warp Convert between maps ↔ fields and between ITK / FSL / ANTs / AFNI; --invert warps.
wk-convert-fieldmap Convert between mm displacement maps/fields and Hz B0 field maps.
wk-compute-jacobian Per-voxel Jacobian determinant (1 = no change, <1 = compression, >1 = expansion).

wk-medic runs the full pipeline; wk-unwrap-phase + wk-compute-fieldmap run the same thing in two stages so you can inspect/reuse the unwrapped phase. Acquisition parameters can come either from BIDS sidecars or from the command line — pick one.

From BIDS sidecars:

wk-medic \
  --magnitude mag_e1.nii.gz mag_e2.nii.gz mag_e3.nii.gz \
  --phase phase_e1.nii.gz phase_e2.nii.gz phase_e3.nii.gz \
  --metadata mag_e1.json mag_e2.json mag_e3.json \
  --out-prefix sub-01_run-01

EchoTime is read per file (seconds → converted to ms internally); TotalReadoutTime and PhaseEncodingDirection are taken from the first sidecar.

Or by passing acquisition parameters directly:

wk-medic \
  --magnitude mag_e1.nii.gz mag_e2.nii.gz mag_e3.nii.gz \
  --phase phase_e1.nii.gz phase_e2.nii.gz phase_e3.nii.gz \
  --TEs 14.2 38.93 63.66 \
  --total-readout-time 0.0501 \
  --phase-encoding-direction j- \
  --out-prefix sub-01_run-01

--TEs is in milliseconds, --total-readout-time in seconds, and --phase-encoding-direction is one of i, j, k, i-, j-, k-, x, y, z, x-, y-, z-.

Run any wk-* CLI with --help for the full option list.

Common follow-on workflows

Apply a MEDIC displacement-map series to the BOLD it was derived from (per-frame distortion correction):

wk-apply-warp \
  --input bold.nii.gz \
  --transform sub-01_run-01_displacementmaps.nii \
  --phase-encoding-axis j \
  --output bold_corrected.nii.gz

Convert MEDIC's per-frame displacement maps into per-frame ANTs-format displacement fields:

wk-convert-warp \
  --input sub-01_run-01_displacementmaps.nii \
  --to field --axis j --to-format ants \
  --output field_{0..14}.nii.gz

Compute the per-frame Jacobian determinant (volume-change map) of those displacement maps:

wk-compute-jacobian \
  --input sub-01_run-01_displacementmaps.nii \
  --axis j \
  --output sub-01_run-01_jacobian.nii

Convert MEDIC's mm displacement maps to a Hz B0 field map (or back — this CLI handles either direction, with maps or fields on the mm side):

wk-convert-fieldmap \
  --input sub-01_run-01_displacementmaps.nii \
  --to fieldmap \
  --total-readout-time 0.0501 \
  --phase-encoding-direction j- \
  --output sub-01_run-01_fieldmap.nii

Citation

If you use warpkit, please cite:

Van AN, Montez DF, Laumann TO, Cho PN, Suljic V, Madison T, et al. Frame-wise multi-echo distortion correction for superior functional MRI. Imaging Neuroscience 2026; 4 IMAG.a.1262. doi: https://doi.org/10.1162/IMAG.a.1262

Authors

Vahdeta Suljic <suljic@wustl.edu>, Andrew Van <vanandrew77@gmail.com>

Project links

Data verified by PyPI on Jul 28, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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Avatar for vanandrew from gravatar.com vanandrew

Credits

Author: Andrew Van

GitHub Statistics

Data verified by PyPI on Jul 28, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.11

Tags

neuroimaging

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The following attestation bundles were made for warpkit-1.5.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build.yml on vanandrew/warpkit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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Details for the file warpkit-1.5.0-cp311-cp311-macosx_10_9_universal2.whl.

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Provenance

The following attestation bundles were made for warpkit-1.5.0-cp311-cp311-macosx_10_9_universal2.whl:

Publisher: build.yml on vanandrew/warpkit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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