Diffusion imaging scripts

This section describes diffusion imaging preprocessing and model estimation scripts of Anima scripts.

Diffusion images preprocessing script

This script combines several preprocessing steps to prepare data with the following steps (in that order):

  • if -D option is given, correct gradients to match requirements from Anima (in real space i.e. the scanner space)
  • Eddy current correction and motion correction using the experimental tool from Anima animaEddyCurrentCorrection
  • distortion correction using the method proposed in [1]
  • reorientation of the DWI volume to be axial on the z-axis and have no reversed axis
  • Denoising using the NL-Means method [2]
  • Brain masking using the brain extraction script
  • DTI estimation using the animaDTIEstimator tool

Some of these steps may be discarded using the --no-\* options available in the script help. The brain masking step is performed either on a provided T1 image or on the DWI first sub-volume. For distortion correction, the reversed PED image has to be provided with the -r option and the direction of the PED with the -d option. If no reversed PED image, and the T1 is, distortion will be corrected by a simple B0 to T1 non linear registration.

Warning: gradients reworking is known to work only with Siemens acquisitions, not tested on other scanners.

Example:

~/Anima-Scripts-Public/diffusion/animaDiffusionImagePreprocessing.py -b Diff.bval -D Dicom/* -r B0_PA.nii.gz -d 1 -t T1.nii.gz -i Diff.nii.gz

Results of this scripts are:

  • Diff_preprocessed.nrrd: preprocessed diffusion 4D image
  • Diff_preprocessed.bvec: preprocessed diffusion gradient vectors
  • Diff_brainMask.nrrd: diffusion brain mask
  • Diff_Tensors.nrrd: estimated tensors
  • Diff_Tensors_B0.nrrd: estimated tensors B0 image
  • Diff_Tensors_NoiseVariance.nrrd: estimated noise variance from the tensor estimation (actually includes noise and model underfit error)

Multi-compartment models estimation script

This script is more experimental as the multi-compartment model estimation tool in Anima [3] is currently undergoing quite some changes. However, it works well and we have a script that uses preferably the results of the diffusion preprocessing script as an input.

It mainly has two modes: one for HCP-like datasets that have high quality acquisitions and enable less constrained models, and one for regular clinical data with less estimation demanding models. Several options are still available:

  • -t: choose the model type (as explained in diffusion documentation
  • -n: maximal number of anisotropic compartments (ideally choose a number in between 1 and 3)
  • –hcp: add some additional compartments like stationary water to handle specific aspects of HCP data
  • –no-model-simplification and -S control model selection / averaging option. If none are set, the script will by default follow the method proposed in [4]: compute all models from 0 to N compartments and “average” them according to their likelihood (according to the AIC criterion). If -S is set, a faster model selection done at the stage of the stick model estimation is performed. If –no-model-simplification is set, a pure N model estimation is done without model selection.

Example:

~/Anima-Scripts-Public/diffusion/animaMultiCompartmentModelEstimation.py -t tensor -n 3 -i Diff_preprocessed.nrrd -g Diff_preprocessed.bvec -b Diff.bval -m Diff_brainMask.nrrd

References

  1. Renaud Hédouin, Olivier Commowick, Elise Bannier, Benoit Scherrer, Maxime Taquet, Simon Warfield, Christian Barillot. Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions. IEEE Transactions on Medical Imaging, in press available online, 2017.
  2. Nicolas Wiest-Daesslé, Sylvain Prima, Pierrick Coupé, Sean Patrick Morrissey, Christian Barillot. Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. 11th International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 5242 (Pt 2), pp.171-179, 2008.
  3. Aymeric Stamm, Olivier Commowick, Simon K. Warfield, Simone Vantini. Comprehensive Maximum Likelihood Estimation of Diffusion Compartment Models Towards Reliable Mapping of Brain Microstructure. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.
  4. Aymeric Stamm, Olivier Commowick, Patrick Pérez, Christian Barillot. Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion. IEEE International Symposium on Biomedical Imaging, 2014.