tomobar 2024.8

TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software

TOmographic MOdel-BAsed Reconstruction software PAPER (CT Meeting 2020) ToMoBAR is a Python and Matlab (not currently maintained) 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 regularisers resulting in complex objectives for tomographic reconstruction. ToMoBAR can handle multi-GPU parallel reconstruction in Python and also device-to-device methods operating on CuPy arrays.

Master	Anaconda binaries

NEW in ToMoBAR since v.2024.01:

• DOCUMENTATION is available. Various tutorials are presented and references to API given.
• CuPy-enabled 3D FISTA-OS with regularisation all in-device implementation. It should be 3-5 times faster than the non-CuPy version depending on the GPU device in use and the size of the data.
• Now one can specify the axes labels to describe the input data so it will be automatically passed in the right format to the method. See this Demo.
• Demos changed to adhere the recent changes in TomoPhantom v.3.0.

Software includes:

• A wrapper around ASTRA-toolbox to simplify access to various reconstruction methods available in ASTRA
• Regularised iterative ordered-subsets FISTA reconstruction algorithm with linear and non-linear data fidelities
• Regularised iterative ADMM reconstruction algorithm
• CuPy driven forward/backward projectors to enable faster device-to-device operations and all in GPU memory protoyping of algorithms
• Access to multi-GPU capability through mpi4py library
• Demos to reconstruct synthetic and also real data [4-6]

ToMoBAR highlights:

Check what ToMoBAR can do.

Software dependencies

All dependencies are listed here.

Installation

Please check the detailed installation guide.

How to use ToMoBAR in Python:

Please see Tutorials and Demos for more details.

References:

1. D. Kazantsev and N. Wadeson 2020. TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software for high resolution synchrotron X-ray tomography. CT Meeting 2020
2. P. Paleo and A. Mirone 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278.
3. 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.
4. 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.
5. H. Om Aggrawal et al. 2017. A Convex Reconstruction Model for X-ray tomographic Imaging with Uncertain Flat-fields", IEEE Transactions on Computational Imaging
6. V. Van Nieuwenhove et al. 2015. Dynamic intensity normalization using eigen flat fields in X-ray imaging. Optics express 23(21).

Applications (where ToMoBAR software have been used or referenced):

7. D. Kazantsev et al. 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.
8. E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.
9. 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
10. E. Guo et al. 2017. Dendritic evolution during coarsening of Mg-Zn alloys via 4D synchrotron tomography. Acta Materialia
11. E. Guo et al. 2017. Synchrotron X-ray tomographic quantification of microstructural evolution in ice cream–a multi-phase soft solid. Rsc Advances
12. Liu Shi et al. 2020. Review of CT image reconstruction open source toolkits, Journal of X-Ray Science and Technology

License:

GNU GENERAL PUBLIC LICENSE v.3

Questions/Comments

can be addressed to Daniil Kazantsev at [e-mail]