Diffusion model pipeline

This pipeline groups all the processing steps necessary to build a diffusion model from a diffusion-weighted acquisition. The model can be a tensor image (DTI) or a QBall model.

Description

Before using this pipeline it is recomended to put your data in a BrainVISA database. See the following process directories for more information :
data management / import / Diffusion and Tracking

Unless you have specific needs, default options of each processing step can be used to run the whole pipeline. However, it is possible to select/deselect or to modify the parameters of each steps. The following steps are included in this pipeline :

Echoplanar Distortion Correction: if you provides both raw_dw_diffusion and corrected_dw_diffusion parameters, this correction step will be applied. This step is not selected by default if corrected_dw_diffusion image already exists.

Brain Mask from T2: this process extract a brain mask from the t2_diffusion image. This mask is used to restric model computation (and therefore maps generation) to voxels in the brain. Voxels which are not in the mask (containing noise) are ignored. The use of a mask is not mandatory, if there is no mask, there is no restriction on processed voxels. This step is applied once if mask parameter is provided. By default it is not selected if mask image already exists.

Diffusion model: this process creates the diffusion model (DTI by default). If you choose a QBall model, by default DTI model will be also computed because it is needed to create diffusion maps. Neithertheless, you can unselect the DTI model step if you're not interested in diffusion maps.

DiffusionMaps: this last step build the main maps (i.e. images) used to analyse DTI signal : ADC, FA, VR, etc.

Parameters

t2_diffusion: T2 Diffusion MR ( input )

T2 image extracted from diffusion raw data.

raw_dw_diffusion: Raw DW Diffusion MR ( optional, input )

Diffusion-weighted raw (i.e., non corrected for echoplanar distortions) images.

corrected_dw_diffusion: Corrected DW Diffusion MR ( optional, output )

Diffusion-weighted images corrected for echoplanar distortions.

mask: Diffusion Mask ( optional, output )

Binary mask used to limit the computed voxels.

model_type: Choice ( input )

Type of model to compute.

diffusion_model: Diffusion Model ( output )

Computed diffusion model.

Technical information

Toolbox : Diffusion and Tracking

User level : 0

Identifier : DiffusionModelPipeline

File name : brainvisa/toolboxes/connectomist/processes/DiffusionModelPipeline.py

Supported file formats :

t2_diffusion :

GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v image

raw_dw_diffusion :

GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v image

corrected_dw_diffusion :

GIS image, VIDA image, NIFTI-1 image, MINC image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v image

mask :

GIS image, VIDA image, NIFTI-1 image, MINC image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v image

diffusion_model :

Bucket