Implements free water elimination (FWE) models for preprocessing diffusion MRI data.

$ git clone https://github.com/nrdg/fwe
$ cd fwe
$ pip install .

The software depends on DIPY, which is installed automatically as part of the installation process specified above.

• Free water DTI as implmented in dipy (Hoy et al., 2014): Use fwe_model="dipy_fwdti"
• Beltrami regularized gradient descent free water DTI (Golub et al., 2020): Use fwe_model="golub_beltrami"

The software expects as input data that has already been preprocessed (we use qsiprep).

If the data has multiple non-zero b-values, it is preferable to use the 'dipy_fwdti' model. If the data has one non-zero b-value, only the 'golub_beltrami' model can be used.

The following is a complete example, using a subject from the HBN POD2 dataset.

To run this example, you will also need the boto3 software library (pip install boto3), which will download the data.

from dipy.data.fetcher import fetch_hbn, dipy_home
import os.path as op
from fwe import free_water_elimination

fetch_hbn(["NDARAA948VFH"])

dwi_folder = op.join(dipy_home, "HBN/derivatives/qsiprep/sub-NDARAA948VFH/ses-HBNsiteRU/dwi/")
data_root = op.join(dwi_folder, "sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi")

free_water_elimination(
  dwi_fname    = data_root + ".nii.gz",
  bval_fname   = data_root + ".bval",
  bvec_fname   = data_root + ".bvec",
  mask_fname   = op.join(dwi_folder, "sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-brain_mask.nii.gz"),
  fwe_model    = "dipy_fwdti",
  output_fname = "sub-01_fwe.nii.gz"
)