Data and code underlying the publication: ''Optimising proton stopping power ratio prediction with spectral cone-beam CT''

DOI:10.4121/bd754669-5af8-4e2d-9a70-37ac56bd6674.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/bd754669-5af8-4e2d-9a70-37ac56bd6674

Datacite citation style

Leibold, David; Schaart, Dennis R.; Goorden, Marlies C. (2025): Data and code underlying the publication: ''Optimising proton stopping power ratio prediction with spectral cone-beam CT''. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bd754669-5af8-4e2d-9a70-37ac56bd6674.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This repository contains the data and code for our paper:

David Leibold, Dennis R. Schaart, and Marlies C. Goorden, "Optimising proton stopping power ratio prediction with spectral cone-beam CT", Phys. Med. Biol., vol. 70, no. 14, p. 145023, 2025, doi: 10.1088/1361-6560/adebd6.

The objective of the publication is a comparison of various spectral cone-beam CT setups in terms of their suitability for proton therapy, more specifically, their ability to predict proton stopping power ratios.

In this repository you can find the data that is presented in our paper, along with the Python code that was used to generate it. For more information please see the README.txt file, which can be opened with any text editor.

History

  • 2025-07-17 first online, published, posted

Publisher

4TU.ResearchData

Format

Raw data: .txt (Plain text) files. Simulation software: .py (Python) files. Auxiliary data: .npy (Numpy) files.

Funding

  • This research was partially funded by Varian, a Siemens Healthineers company, grant number 2018016.

Organizations

TU Delft, Faculty of Applied Sciences, Department of Radiation Science and Technology

DATA

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