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TOmographic MOdel-BAsed Reconstruction software PAPER (CT Meeting 2020)
ToMoBAR is a library of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability. ToMoBAR offers you a selection of various data models and constraints resulting in more complex yet versatile objectives. |
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A wrapper around ASTRA-toolbox to simplify access to various reconstruction methods ASTRA has
Regularised iterative ordered-subsets FISTA reconstruction algorithm with linear and non-linear data fidelities
Regularised iterative ADMM reconstruction algorithm
Demos to reconstruct synthetic and also real data (provided) [4-6]
- Tomographic parallel-beam 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; data fidelities: PWLS, Kullback-Leibler, Huber, Group-Huber[2], Students't [3,4], and SWLS [5] to deal with noise and imaging artifacts (rings, streaks).
- MATLAB or Python
- ASTRA-toolbox for projection operations
- TomoPhantom for tomographic data and phantoms simulations
- CCPi-RegularisationToolkit for regularisation [7]
- See INSTALLATION for detailed information
For building on Linux see run.sh
Install from the conda channel:
conda install -c dkazanc tomobar
or build with:
export VERSION=`date +%Y.%m` (unix) / set VERSION=2020.10 (Windows)
conda build conda-recipe/ --numpy 1.15 --python 3.7
conda install -c file://${CONDA_PREFIX}/conda-bld/ tomobar --force-reinstall
conda install tomobar --use-local --force-reinstall # if Python2
Simply use available m-functions, see Demos
- D. Kazantsev and N. Wadeson 2020. TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software for high resolution synchrotron X-ray tomography. CT Meeting 2020
- P. Paleo and A. Mirone 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278.
- 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.
- 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.
- H. Om Aggrawal et al. 2017. A Convex Reconstruction Model for X-ray tomographic Imaging with Uncertain Flat-fields", IEEE Transactions on Computational Imaging
- V. Van Nieuwenhove et al. 2015. Dynamic intensity normalization using eigen flat fields in X-ray imaging. Optics express 23(21).
- D. Kazantsev et al. 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.
- E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.
- 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
- E. Guo et al. 2017. Dendritic evolution during coarsening of Mg-Zn alloys via 4D synchrotron tomography. Acta Materialia
- E. Guo et al. 2017. Synchrotron X-ray tomographic quantification of microstructural evolution in ice cream–a multi-phase soft solid. Rsc Advances
- Liu Shi et al. 2020. Review of CT image reconstruction open source toolkits, Journal of X-Ray Science and Technology
GNU GENERAL PUBLIC LICENSE v.3
can be addressed to Daniil Kazantsev at dkazanc@hotmail.com