Regularised FISTA-type iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements

This software supports research published in the following journal papers [1,2,3] with applications in [4-6]. Software depends on several software packages and requires a GPU (Nvidia) card to operate. FISTA-tomo is implemented in both MATLAB and Python.

Software highlights:

• Tomographic projection data are simulated without the "inverse crime" using TomoPhantom. Noise and artifacts (zingers, rings) can be modelled and added to data.
• Simulated data reconstructed iteratively using FISTA-type algorithm with multiple "plug-and-play" regularisers from CCPi-RegularisationToolkit
• Presented FISTA algorithm offers novel modifications: convergence acceleration with ordered-subsets method, PWLS, Group-Huber[3] and Students't data fidelities [1,2] to deal with noise and image artifacts
• Various projection (2D/3D) geometries are supported and real data provided to demonstrate the effectiveness of the method

General software prerequisites

• MATLAB or
• Python
• C compilers (GCC/MinGW) and nvcc CUDA SDK compilers

Software dependencies:

• ASTRA-toolbox for projection operations
• TomoPhantom for simulation
• CCPi-RegularisationToolkit for regularisation
• See INSTALLATION for detailed information

Installation in Python (conda):

Install with conda install -c dkazanc fista-tomo or build with:

conda build Wrappers/Python/conda-recipe --numpy 1.12 --python 3.5 
conda install fista-tomo --use-local --force

Package contents:

• A number of demos for 2D/3D parallel and cone-beam geometry with 2D and 3D regularisation routines using CCPi-RegularisationToolkit. Demos show how the methods deal with noise and artifacts. Also real-data example added to emphasise methods properties.

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:

4. E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.
5. 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, vol.237
6. E. Guo et al. 2017. Dendritic evolution during coarsening of Mg-Zn alloys via 4D synchrotron tomography. Acta Materialia, 123, pp.373-382.
7. E. Guo et al. 2017. Synchrotron X-ray tomographic quantification of microstructural evolution in ice cream–a multi-phase soft solid. Rsc Advances, 7(25), pp.15561-15573.

License:

GNU GENERAL PUBLIC LICENSE v.3

Questions/Comments

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