CT scan data underlying the PhD dissertation: Numerical and deep learning algorithms for automated quality assurance in proton therapy

DOI:10.4121/b15ea962-a023-468b-9ec8-c7be90329d7a.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/b15ea962-a023-468b-9ec8-c7be90329d7a

Datacite citation style

Burlacu, Tiberiu (2025): CT scan data underlying the PhD dissertation: Numerical and deep learning algorithms for automated quality assurance in proton therapy. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/b15ea962-a023-468b-9ec8-c7be90329d7a.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The dataset contains CT scans used as input for the algorithm developed in chapters 2 and 3 of the dissertation. Using a CT scan and a treatment plan as inputs, dose and dose change computations in regions of interest can be performed. Specifically, the dataset contains:


  • a head and neck CT scan obtained from the CORT dataset [1],
  • a prostate CT scan obtained from the Cancer Imaging Archive [2],
  • and multiple self-made custom water box CT scans. In addition to a homogeneous water box CT scan (i.e., a cube with uniform 0 Hounsfield Units (HU) composition), there are scans where a slab with half the side-length of the cube and composition of either bone (1000 HU) or air (-1000 HU) is inserted in the water box at varying distances from the middle point.


All the scans consist of CT slices and are stored in the DICOM format. To correctly read, relate to each other and further process the different CT slices, appropriate DICOM reading software (e.g., the pydicom Python package) must be used.


References:

[1] - Craft, D., Bangert, M., Long, T., Papp, D., & Unkelbach, J. (2014). Supporting material for: "Shared data for IMRT optimization research: the CORT dataset" [Data set]. GigaScience Database. https://doi.org/10.5524/100110

[2] - Yorke, A. A., McDonald, G. C., Solis, D., & Guerrero, T. (2019). Pelvic Reference Data (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.WOSKQ5OO

History

  • 2025-02-14 first online, published, posted

Publisher

4TU.ResearchData

Format

zipped DICOM files

Organizations

TU Delft, Faculty of Applied Sciences, Department of Radiation Science & Technology, Medical Physics & Technology

DATA

Files (4)