warpkit

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:

and turns it into a per-frame 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:

CLI

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

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>