Code for tomographic reconstruction built around the ASTRA toolbox (https://www.astra-toolbox.com/). This code implements model-based computed tomography (CT) reconstruction algorithms based on a Markov random field (MRF) prior and requires systems with atleast one GPU. The algorithms involve formulating and finding a minimum of the cost function of the form:
where
astra toolbox : Core GPU based projection and back-projection
numpy, scipy, matplotlib, time, gc, concurrent, psutil, ctypes
pyqtgraph (optional): For displaying 3D volumes
gcc
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Install the above packages (
conda env create -f environment.yml --name pymbir python=3.7
creates a fresh conda environment with the dependencies installed) -
Run
conda activate pymbir
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From the base folder, run
python setup.py install
We highly recommend starting with the examples in the examples/sim directory to develop an understanding of the pyMBIR package and how to set the different parameters.
[1] Thibault, Jean‐Baptiste, et al. "A three‐dimensional statistical approach to improved image quality for multislice helical CT." Medical physics 34.11 (2007): 4526-4544.
[2] Venkatakrishnan, Singanallur V., et al. "Model-based iterative reconstruction for neutron laminography." 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017.
[3] Singanallur Venkatakrishnan, Puneet Juneja, Hugh O’Neill, “Model-based Reconstruction for Single Particle Cryo-Electron Microscopy”, Proc. of IEEE Asilomar Conference of Signals, Systems and Computer 2020
pyMBIR is distributed as open-source software under a GPL License (see the LICENSE file for details)