Difference between revisions of "DTI pre-processing"
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Diffusion Tensor Imaging depicts the anisotropy of tissue. | 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), a structural T1 image | + | 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 <code>perform_dti_processing.py</code> and its help message is the following: | The nipype script to generate the DTI is <code>perform_dti_processing.py</code> and its help message is the following: | ||
| Line 46: | Line 46: | ||
--rigid Only use rigid registration for DWI (no eddy current | --rigid Only use rigid registration for DWI (no eddy current | ||
correction) | correction) | ||
| + | </pre> | ||
| + | |||
| + | A 4D DWI image, corresponding to a single diffusion acquisition is necessarily associated with its corresponding set of bval/bvec. | ||
| + | The script <code>perform_dti_processing.py</code> 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: | ||
| + | <pre>perform_dti_processing.py -i DWI -a bval -e bvec -t T1 -m mag -p phase -o output_dir</pre> | ||
| + | |||
| + | 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: | ||
| + | <pre>perform_dti_processing.py -i DWI1 DWI2 -a bval1 bval2 -e bvec1 bvec2 -t T1 -m mag -p phase -o output_dir</pre> | ||
| + | |||
| + | |||
| + | The outputs are gathered in the <code>-o</code> argument (e.g. <code>output_dir</code>. All output files share a similar prefix in their filename, corresponding to the <code>basename</code> of the input DWI file. It usually corresponds to a subject identifier (e.g. <code>subject_id</code> | ||
| + | |||
| + | Here is a list of the principal outputs and their corresponding description: | ||
| + | <pre> | ||
| + | _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) | ||
</pre> | </pre> | ||
Latest revision as of 11:28, 28 August 2015
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)
