/ToMoBAR

TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Master Anaconda binaries
Build Status conda version conda last release conda platforms conda dowloads

TOmographic MOdel-BAsed Reconstruction (ToMoBAR)

ToMoBAR is a library of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability

Software includes:

#1589F0 A wrapper around ASTRA-toolbox to simplify access to various reconstruction methods

#1589F0 Regularised iterative ordered-subsets FISTA reconstruction algorithm with linear and non-linear data fidelities

#1589F0 Regularised iterative ADMM reconstruction algorithm

#1589F0 Demos to reconstruct synthetic and also real data (provided) [4-6]



Software highlights:

  • Tomographic projection data can be simulated without the "inverse crime" using TomoPhantom. Noise and artifacts (zingers, rings, jitter) can be modelled and added to the data.
  • Simulated data reconstructed iteratively using FISTA or ADMM algorithms with multiple "plug-and-play" regularisers from CCPi-RegularisationToolkit.
  • The FISTA algorithm offers various modifications: convergence acceleration with ordered-subsets method, PWLS, Huber, Group-Huber[3] and Students't data fidelities [1,2] to deal with noise and imaging artifacts (rings, streaks).

General software prerequisites

Software dependencies:

Installation (Python or Matlab)

Python standalone

For building on Linux see run.sh

Python conda:

Install from the conda channel:

conda install -c dkazanc tomobar

or build with:

export VERSION=`date +%Y.%m` (unix) / set VERSION=2019.11 (Windows)
conda build conda-recipe/ --numpy 1.16 --python 3.6
conda install tomobar --use-local --force-reinstall # if this doesn't work
conda install -c file://${CONDA_PREFIX}/conda-bld/ tomobar # try this one

Matlab:

Simply use available m-functions, see Demos

References:

  1. D. Kazantsev et al. 2017. A Novel Tomographic Reconstruction Method Based on the Robust Student's t Function For Suppressing Data Outliers. IEEE TCI, 3(4), pp.682-693.
  2. D. Kazantsev et al. 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9), p.094004.
  3. P. Paleo and A. Mirone, 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278.

Applications (where ToMoBAR software have been used):

  1. D. Kazantsev et al. 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.
  2. E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.
  3. E. Guo et al. 2018. Revealing the microstructural stability of a three-phase soft solid (ice cream) by 4D synchrotron X-ray tomography. Journal of Food Engineering
  4. E. Guo et al. 2017. Dendritic evolution during coarsening of Mg-Zn alloys via 4D synchrotron tomography. Acta Materialia
  5. E. Guo et al. 2017. Synchrotron X-ray tomographic quantification of microstructural evolution in ice cream–a multi-phase soft solid. Rsc Advances

License:

GNU GENERAL PUBLIC LICENSE v.3

Questions/Comments

can be addressed to Daniil Kazantsev at dkazanc@hotmail.com