This Python code allows to reproduce the results of Robust Image Reconstruction with Misaligned Structural Information [1].
[1] L. Bungert and M. J. Ehrhardt (2020). Robust Image Reconstruction with Misaligned Structural Information. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3043638.
The aim of [1] is to reconstruct an image from an indirect measurement whilst registering it with a structural side information from a different modality.
Our code requires the Operator Discretization Library (ODL) https://odlgroup.github.io/odl/index.html. Furthermore, the scripts mri_multi_step_method.py, ct_multi_step_method.py, and ct_rotations_multi_step_method.py, which implement a three-step reconstruction and registration method call MATLAB which requires version compatibility (https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/support/sysreq/files/python-support.pdf) and an installation of the MATLAB engine (https://de.mathworks.com/help/matlab/matlab-engine-for-python.html). Since the three-step method only serves as comparison for our proposed method, these scripts are not essential.
There are a number of scripts which reproduce the results as presented in the paper. First you have to unpack the raw data by running process_data.py in every subfolder of raw_data.
To produce all images involved in the figures of [1], simply run the respective scripts in code, for instance, RUNME_figure_3_4.py.
This calls the following scripts which can be called on their own and can be adapted to individual purposes:
- mri_affreg_multigrid.py
- mri_multi_step_method.py
- ct_affreg_multigrid.py
- ct_multi_step_method.py
- ct_rotations_affreg_multigrid.py
- ct_rotations_multi_step_method.py
- hs_affreg_multigrid.py
[1] L. Bungert and M. J. Ehrhardt (2020). Robust Image Reconstruction with Misaligned Structural Information. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3043638.