2DeteCTCodes

This is a collection of Python scripts for loading, pre-processing, reconstructing and segmenting X-ray CT projection data of the 2DeteCT data collection as described in

Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka "2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", Sci Data 10, 576 (2023) or arXiv:2306.05907 (2023)

• Sinogram_production_2DeteCT.ipynb was used to produce the sinograms of the 2DeteCT data collection from the raw measurements.

• Reconstructions_2DeteCT_2048_allMeth encompasses code for various reconstruction methods ('FBP', 'LS', 'NNLS', 'SIRT', 'SART', 'CGLS', 'AGD') to compute reconstructions for the sinogram data of the 2DeteCT data collection.

• NesterovGradient.py contains the implementation of an accelerated gradient descent (AGD) iterative reconstruction by Henri Der Sarkissian.

• Reconstructions_2DeteCT.py was used to produce the reference reconstructions of the 2DeteCT data collection from the sinogram data of the first bullet point using the above AGD iterative reconstruction.

• Segmentation_2DeteCT.ipynb was used to produce the reference segmentations of the 2DeteCT data collection based on the reconstructions of ‘mode 2’ from the bullet point above.

• Mode1_settings.csv, Mode2_settings.csv, Mode3_settings.csv are machine-readable settings files for the 2DeteCT data collection acquisition.

• ReadingSettings_2DeteCT.py contains a class for reading in the 2DeteCT acquisition settings from the above mentioned .csv files.

• The complete data collection can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370.

• Each slice folder ‘slice00001 - slice05000’ and ‘slice05521 - slice06370’ contains three folders for each mode: ‘mode1’, ‘mode2’, ‘mode3’. In each of these folders there are the sinogram, the dark-field, and the two flat-fields for the raw data archives, or just the reconstructions and for mode2 the additional reference segmentation.

• The corresponding reference reconstructions and segmentations can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370.

Requirements

• Most of the above scripts make use of the ASTRA toolbox. If you are using conda, this is available through the astra-toolbox/ channel.

Contributors

Maximilian Kiss (maximilian.kiss@cwi.nl), CWI, Amsterdam, Henri Der Sarkissian (henri.dersarkissian@gmail.com), Felix Lucka (Felix.Lucka@cwi.nl), CWI, Amsterdam