warpkit 0.0.1

A python library for neuroimaging transformations

warpkit

A python library for neuroimaging transforms

If you've landed here, you're probably interested in the Multi-Echo DIstortion Correction (MEDIC) algorithm, which this library implements.

You can find the pre-print for MEDIC here: https://www.biorxiv.org/content/10.1101/2023.11.28.568744v1.

See below for usage details.

Installation

Installing through conda (recommended)

The easiest way to install warpkit is through a conda environment. Currently, warpkit is not uploaded to any conda channels, so you will need to build it from source.

[!NOTE] For advanced users only A meta.yaml file is present in the conda folder in the root of the repo. If you are familiar with conda-build you should be able to build this with your existing conda install.

Fortunately, there is an automated script that will download micromamba (a conda variant), create a new environment, and build/install warpkit for you automagically. To do this, clone this repo and run the following:

# switch to conda directory in repo
cd conda
# run the install script (use -p or --path to specify an install prefix)
# by default this is set to ~/micromamba
./warpkit-conda-install.sh

# in your shell profile add the following lines
# or just execute them in your current shell
# but it won't be permanent
export MAMBA_ROOT_PREFIX=/whatever/path/you/installed/micromamba/prefix/above
eval "$(/whatever/path/you/installed/micromamba/prefix/above/bin/micromamba shell hook -s posix)"

# if you added the above to your profile
# restart your shell then activate the environment
#
# unless you have already executed the above in your current shell
# then you don't need to restart it and can just activate the environment
micromamba activate base

# you should now be able to import/use warpkit
python -c "import warpkit"  # should return no errors
medic --help  # should return help

After installing and configuring everything, you must type micromamba activate base each time you open a new terminal to get back into the conda environment to use warpkit. To make this permanent, you can add the above line to your shell's profile file.

Installing through pip

To install, clone this repo and run the following in the repo directory. Due to the current developmental nature of this package, I highly recommend installing it in editable mode (with the strict option, see here):

pip install -e ./[dev] -v --config-settings editable_mode=strict

You will need a C++ compiler with C++17 support, as well as Julia pre-installed on your system. For the Julia install, ensure that you have the julia executable in your path, and the julia libraries correctly setup in your ld.so.conf. If you installed julia via a package manager, this should be done for you (most of the time) already. However, if you installed Julia manually, you may need to tell ldconfig where the julia libraries are. For example, on debian based systems you can do this with:

# /path to julia installation (the lib folder will have libjulia.so)
echo /path/to/julia/lib > /etc/ld.so.conf.d/julia.conf
ldconfig

If you have done this correctly, you should see libjulia.so in your ldconfig:

ldconfig -p | grep julia                                                                                        
	libjulia.so.1 (libc6,x86-64) => /usr/lib/libjulia.so.1
	libjulia.so (libc6,x86-64) => /usr/lib/libjulia.so

The above may require root privileges. The alternative to the above is to set the LD_LIBRARY_PATH environment variable to the path of the julia libraries.

# /path to julia installation (the lib folder will have libjulia.so)
export LD_LIBRARY_PATH=/path/to/julia/lib:$LD_LIBRARY_PATH

Note however, that you must type the above each time you open a new terminal. To make this permanent, you can add the above line to your shell's profile file.

The build process uses CMake to build the C++/Python Extension. If you encounter an error during the build process, please report the full logs of the build process using the -v flag to the pip command above.

What is MEDIC?

MEDIC takes your ME-EPI phase data from this:

to this:

for each frame of your data. You can then use these field maps to distortion correct your data.

MEDIC Usage

The warpkit library is meant for integration into a larger neuroimaging pipeline/software package.

An example of how to call MEDIC from python is provided below:

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

# load phase and magnitude images into lists
# each element in list 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, ...] # in milliseconds
effective_echo_spacing = ... # in seconds
phase_encoding_direction = either i, j, k, i-, j-, k-, x , y, z, x-, y-, z- 

# call the medic function
field_maps_native, displacement_maps, field_maps = medic(
    phases, magnitudes, TEs, effective_echo_spacing, phase_encoding_direction)

# field_maps_native are returned in the distorted space (Hz) (mainly for reference/debugging purposes)
# you shouldn't need to use these probably???
# displacement_maps are returned in the undistorted space (mm) (see below for usage)
# field_maps are returned in the undistorted space (Hz) (same field map output as topup/fugue, but framewise)

# returns are nibabel Nifti1Image objects, so they can be saved to file by:

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

# these should be converted to displacement fields
# by the displacement_map_to_field function
# and specifying the appropriate type (i.e. itk, ants, afni, fsl)

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

# where axis specifies the phase encoding direction, format is the desired output format, and frame is the index of
# displacement map to convert to a displacement field

# the displacement field can then be saved to file by the to_filename method
# Each file can be applied with the respective software's displacement field application tool:
# itk: the internal format warpkit uses. See utilities.resample_image
# ants: antsApplyTransforms (note that even though ants also uses itk, warpkit's itk warp format is NOT equivalent)
# afni: 3dNwarpApply
# fsl: applywarp

# if you are using fsl and instead want to use fugue to distortion correction, you can use the field_maps outputs
# (these are the equivalent field maps of that you would get from fugue, but with multiple frames)

You can also use the provided CLI script medic to run MEDIC from the command line. The script is installed to your PATH when you install the package. medic takes the following arguments:

usage: medic [-h] --magnitude MAGNITUDE [MAGNITUDE ...] --phase PHASE
             [PHASE ...] --metadata METADATA [METADATA ...]
             [--out_prefix OUT_PREFIX] [-f NOISEFRAMES] [-n N_CPUS]
             [--debug] [--wrap_limit]

Multi-Echo DIstortion Correction

options:
  -h, --help            show this help message and exit
  --magnitude MAGNITUDE [MAGNITUDE ...]
                        Magnitude data
  --phase PHASE [PHASE ...]
                        Phase data
  --metadata METADATA [METADATA ...]
                        JSON sidecar for each echo
  --out_prefix OUT_PREFIX
                        Prefix to output field maps and displacment maps.
  -f NOISEFRAMES, --noiseframes NOISEFRAMES
                        Number of noise frames
  -n N_CPUS, --n_cpus N_CPUS
                        Number of CPUs to use.
  --debug               Debug mode
  --wrap_limit          Turns off some heuristics for phase unwrapping

Vahdeta Suljic <[e-mail]>, Andrew Van <[e-mail]>
12/09/2022