This file is part of the TVR-DART Toolbox
Author: Dr. Xiaodong ZHUGE
Copyright: 2016, CWI, Amsterdam
License: Open Source under GPLv3
Contact: [email protected] / [email protected]
This is a Python implementation of TVR-DART algorithm (Total Variation Regularized Discrete Algebraic Reconstruction Technique), a robust and automated reconsturction algorithm for performing discrete tomography under limited data conditions. Currently we support 2D and 3D parallel beam geometries, orianted for electron tomography
The basic forward and backward projection operations are GPU-accelerated by utilizing the python interface of the ASTRA tomography toolbox (http://www.astra-toolbox.com/)
See the Python code samples: s01_recon2D.py s02_recon3D.py
The example scripts uses an electron tomography dataset of a Lanthanide-based inorganic nanotube. This data is kindly provided by Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Jülich, Germany. This file is too big for Github though, so please download the data via the following link and passphrase: https://oc.cwi.nl/index.php/s/w099A7BuTGrJzjl passphrase: nanotube
If you use the TVR-DART Toolbox for your research, we would appreciate it if you would refer to the following papers:
[1] X. Zhuge, W.J. Palenstijn, K.J. Batenburg, "TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation," IEEE Transactions on Imaging Processing, 2016, vol. 25, issue 1, pp. 455-468.
[2] X. Zhuge, H. Jinnai, R.E. Dunin-Borkowski, V. Migunov, S. Bals, P. Cool, A.J. Bons, K.J. Batenburg, "Automated discrete electron tomography - Towards routine high-fidelity reconstruction of nanomaterials," Ultramicroscopy, Volume 175, April 2017, Pages 87–96