SynthRAD2025 Grand Challenge dataset: generating synthetic CT for radiotherapy

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

Dataset Composition

The number of cases collected at each center for training, validation, and test sets.

Training Set

Validation Set

Testing Set

Pre-processing
The 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 Design
The overall challenge design can be found at: https://doi.org/10.5281/zenodo.14051074.

Funding
The 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.