DTI pre-processing

Diffusion Tensor Imaging depicts the anisotropy of tissue.

The acquisition protocol includes 6 or more diffusion weighted images (DWIs), one or more B null image (B0), all encoded into a 4D nifti file, its corresponding bval-bvec pair, a structural T1 image, an optional brain mask, and and optional field maps (FM) magnitude and phase that are used for susceptibility correction.

The nipype script to generate the DTI is perform_dti_processing.py and its help message is the following:

usage: perform_dti_processing.py [-h] -i dwis [dwis ...] -a bvals [bvals ...]
                                 -e bvecs [bvecs ...] -t t1 [-m fieldmapmag]
                                 [-p fieldmapphase] [-o output_dir] [-g]
                                 [--rot rot] [--etd etd] [--ped [ped]]
                                 [--rigid]

Perform Diffusion Model Fitting with pre-processing steps. Mandatory Inputs
are the Diffusion Weighted Images and the bval/bvec pair. as well as a T1
image are extracted for reference space. The Field maps are provided so
susceptibility correction is applied. Values to use for the susceptibility
parameters: ## DRC ## (--ped=-y --etd=2.46 --rot=34.56) and ## 1946 ##
(--ped=-y --etd=2.46 --rot=25.92). Note that these values are indicative.

optional arguments:
  -h, --help            show this help message and exit
  -i dwis [dwis ...], --dwis dwis [dwis ...]
                        Diffusion Weighted Images in a 4D nifti file
  -a bvals [bvals ...], --bvals bvals [bvals ...]
                        bval file to be associated with the DWIs
  -e bvecs [bvecs ...], --bvecs bvecs [bvecs ...]
                        bvec file to be associated with the DWIs
  -t t1, --t1 t1        T1 file to be associated with the DWIs
  -m fieldmapmag, --fieldmapmag fieldmapmag
                        Field Map Magnitude image file to be associated with
                        the DWIs
  -p fieldmapphase, --fieldmapphase fieldmapphase
                        Field Map Phase image file to be associated with the
                        DWIs
  -o output_dir, --output_dir output_dir
                        Output directory containing the registration result
                        Default is a directory called results
  -g, --graph           Print a graph describing the node connections
  --rot rot             Diffusion Read-Out time used for susceptibility
                        correction Default is 34.56
  --etd etd             Echo Time difference used for susceptibility
                        correction Default is 2.46
  --ped [ped]           Phase encoding direction used for susceptibility
                        correction (x, y or z) --ped=val form must be used for
                        -ve indicesDefault is the -y direction (-y)
  --rigid               Only use rigid registration for DWI (no eddy current
                        correction)

A 4D DWI image, corresponding to a single diffusion acquisition is necessarily associated with its corresponding set of bval/bvec. The script perform_dti_processing.py is designed to run for one subject only.

So, in most cases you have only one file per option, 1 4D-DWI, 1 T1, 1 bval, 1 bvec, etc, leading to the following command line:

perform_dti_processing.py -i DWI -a bval -e bvec -t T1 -m mag -p phase -o output_dir

In some cases, there are repeated (say 2) DWI acquisitions, in which case you have 2 sets of 4D-DWI, bval, bvec, but only one of the other acquisitions (T1, mag, phase). In this situation you can use:

perform_dti_processing.py -i DWI1 DWI2 -a bval1 bval2 -e bvec1 bvec2 -t T1 -m mag -p phase -o output_dir

The outputs are gathered in the -o argument (e.g. output_dir. All output files share a similar prefix in their filename, corresponding to the basename of the input DWI file. It usually corresponds to a subject identifier (e.g. subject_id

Here is a list of the principal outputs and their corresponding description:

_corrected_dwi: pre-processed merged diffusion weighted images in a 4D nifti file. (motion, susceptibility, etc)
_corrected_dwi.bval/bvec: corresponding bvalues and gradient vector direction files
_average_b0: the average B-Null image
_dwi_to_b0_rotation.png / _interslice_ncc.png : QC plots describing respectively the subject motion and the signal dropouts throughout the DWI scans
_tensors.nii.gz : output fitted tensor image file, in a 4D format with 6 degrees as a 4th dimension, from which the diffusion based biomarker maps are estimated
_famap : the fractional anisotropy map (3D)
_mdmap : the mean diffusivity map (3D)
_v1map : the first eigen vector component map (3D x 3)
_rgbmap : the directional colour coded RGB FA map.
_mask : mask used to crop background during processing, in the diffusion space
_T1/T2_to_B0.txt : affine transformations between T1/T2 and the average B0 image (with the T1 as reference)