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ZENODO
Dataset . 2025
License: CC BY NC
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY NC
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC
Data sources: Datacite
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SynthRAD2025 Grand Challenge dataset: generating synthetic CT for radiotherapy

Authors: Thummerer, Adrian; van der Bijl, Erik; Galapon, Arthur Jr.; Kamp, Florian; Maspero, Matteo;

SynthRAD2025 Grand Challenge dataset: generating synthetic CT for radiotherapy

Abstract

Dataset Description Dataset Structure A detailed description available in "SynthRAD2025_dataset_description.pdf". A paper describing the dataset has been submitted to Medical Physics and is available as pre-print at: https://arxiv.org/abs/2502.17609. The dataset is divided into two tasks: Task 1 (MRI-to-CT conversion) is provided in Task1.zip. Task 2 (CBCT-to-CT conversion) is provided in Task2.zip. After extraction, the dataset is organized as follows: Within each task, cases are categorized into three anatomical regions: Head-and-neck (HN) Thorax (TH) Abdomen (AB) Each anatomical region contains individual patient folders, named using a unique seven-letter alphanumeric code:[Task Number][Anatomy][Center][PatientID]Example: 1HNA001 Each patient folder in the training dataset contains (for other sets see Table below): ct.mha: preprocessed CT image mr.mha or cbct.mha (depending on the task): preprocessed MR or CBCT image mask.mha: Binary mask of the patient outline (dilated) An overview folder within each anatomical region contains: [task]_[anatomy]_parameters.xlsx: Imaging protocol details for each patient. [task][anatomy][center][PatientID]_overview.png: A visualization of axial, coronal, and sagittal slices of CBCT/MR, CT, mask, and difference images. Dataset Overview The SynthRAD2025 dataset is part of the second edition of the SynthRAD deep learning challenge (https://synthrad2025.grand-challenge.org/), which benchmarks synthetic CT generation for MRI- and CBCT-based radiotherapy workflows. Task 1: MRI-to-CT conversion for MR-only and MR-guided photon/proton radiotherapy, consisting of 890 MRI-CT pairs. Task 2: CBCT-to-CT conversion for daily adaptive radiotherapy workflows, consisting of 1,472 CBCT-CT pairs. Imaging data was collected from five European university medical centers: Netherlands: UMC Groningen, UMC Utrecht, Radboud UMC Germany: LMU Klinikum Munich, UK Cologne All centers have independently approved the study in accordance with their institutional review boards or medical ethics committee regulations. Inclusion criteria: Patients treated with external beam radiotherapy (photon or proton therapy) at one of the data-providing centers. Imaging data available from one of the three anatomical regions. No restrictions on age, sex, tumor characteristics, or staging. License The dataset is provided under two different licenses: Data from centers A, B, C, and E is provided under a CC-BY-NC 4.0 International License (creativecommons.org/licenses /by-nc/4.0/). Data from center D is provided with a limited license which permits it's use only for the duration of the challenge and remains valid only while the challenge is active (Limited Use License Center D). By downloading Center D's data, participants agree to these terms. Once the challenge ends, access to the data ends, the download link will be deactivated, and all downloaded data must be deleted. After requesting participation in the challenge on the SynthRAD2025 website, participants can access the download link for center D at https://synthrad2025.grand-challenge.org/data/. Data Release Schedule Subset Files Release Date Link Training Input, CT, Mask 01-03-2025 https://doi.org/10.5281/zenodo.14918213 Training Center D Input, CT, Mask 01-03-2025 Check the download link at:https://synthrad2025.grand-challenge.org/data/Limited use License:License Validation Input Input, Mask 01-06-2025 https://doi.org/10.5281/zenodo.14918504 Validation Input Center D Input, Mask 01-06-2025 Check the download link at:https://synthrad2025.grand-challenge.org/data/Limited use License:License Validation Ground Truth CT, Deformed CT 01-03-2030 https://doi.org/10.5281/zenodo.14918605 Test Input, CT, Deformed CT, Mask 01-03-2030 https://doi.org/10.5281/zenodo.14918722 Dataset Composition The number of cases collected at each center for training, validation, and test sets. Training Set Task Center HN TH AB Total 1 A 91 91 65 247 B 0 91 91 182 C 65 0 19 84 D 65 0 0 65 E 0 0 0 0 Total 221 182 175 578 2 A 65 65 64 195 B 65 65 65 195 C 65 63 62 190 D 65 63 53 181 E 65 65 65 195 Total 325 321 309 955 Validation Set Task Center HN TH AB Total 1 A 14 14 10 38 B 0 14 14 28 C 10 0 3 13 D 10 0 0 10 E 0 0 0 0 Total 34 28 27 89 2 A 10 10 10 30 B 10 10 10 30 C 10 10 10 30 D 10 10 8 28 E 10 10 10 30 Total 50 50 48 148 Testing Set Task Center HN TH AB Total 1 A 35 35 25 95 B 0 35 35 70 C 25 0 8 33 D 25 0 0 25 E 0 0 0 0 Total 85 70 68 223 2 A 25 25 25 75 B 25 25 25 75 C 25 25 25 75 D 25 24 20 69 E 25 25 25 75 Total 125 124 120 369 Pre-processingThe following pre-processing steps were applied: DICOM-to-MHA conversion Rigid registration between CT and MR/CBCT Defacing Resampling to 1×1×3 mm resolution Cropping to remove background and reduce file size Deformable image registration (validation & test sets only) Preprocessing scripts are available at: https://github.com/SynthRAD2025/preprocessing. Challenge DesignThe overall challenge design can be found at: https://doi.org/10.5281/zenodo.14051074. FundingThe challenge is supported by a grant from Stiftungen zu Gunsten der Medizinischen Fakultät der Ludwig-Maximilians-Universität München, awarded to Adrian Thummerer to cover computational costs.

Keywords

Artificial intelligence, Radiotherapy, Image processing, Artificial Intelligence, Magnetic Resonance Imaging, Computed tomography

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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