SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen

Thummerer A, van der Bijl E, Galapon AJ, Kamp F, Savenije M, Muijs C, Aluwini S, Steenbakkers RJ, Beuel S, Intven MP, Langendijk JA, Both S, Corradini S, Rogowski V, Terpstra M, Wahl N, Kurz C, Landry G, Maspero M (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Medical Physics Wiley

Book Volume: 52

Article Number: e17981

Journal Issue: 7

DOI: 10.1002/mp.17981

Abstract

Purpose: Medical imaging is crucial in modern radiotherapy, aiding diagnosis, treatment planning, and monitoring. The development of synthetic imaging techniques, particularly synthetic computed tomography (sCT), continues to attract interest in radiotherapy. The SynthRAD2025 dataset and the accompanying SynthRAD2025 Grand Challenge aim to stimulate advancements in synthetic CT generation algorithms by providing a platform for comprehensive evaluation and benchmarking of synthetic CT generation algorithms based on cone-beam CTs (CBCT) and magnetic resonance images (MRI). Acquisition and validation methods: The dataset comprises 2362 cases, including 890 MRI-CT pairs and 1472 CBCT-CT pairs of head-and-neck, thoracic, and abdominal cancer patients treated at five European university medical centers [UMC Groningen, UMC Utrecht, Radboud UMC (Netherlands), LMU University Hospital Munich, and University Hospital of Cologne (Germany)]. Images were acquired using a wide range of acquisition protocols and scanners. Pre-processing, including rigid and deformable image registration methods, was performed to ensure high-quality image datasets and alignment between modalities. Extensive quality assurance was performed to validate image consistency and usability. Data format and usage notes: All imaging data is provided using the MetaImage (.mha) file format, ensuring compatibility with common medical image processing tools. Metadata, including acquisition parameters and registration details, is available in structured comma-separated value (CSV) files. To ensure dataset integrity, SynthRAD2025 is split into training (65%), validation (10%), and test (25%) sets. The dataset is accessible through https://doi.org/10.5281/zenodo.14918088 under the SynthRAD2025 collection. Potential applications: This dataset enables benchmarking and development of synthetic imaging techniques for radiotherapy applications. Potential use cases include sCT generation for MRI-only and MR-guided photon and proton radiotherapy, CBCT-based dose calculations, and adaptive radiotherapy workflows. By incorporating data from diverse acquisition settings, SynthRAD2025 supports the advancement of robust and generalizable image synthesis algorithms for clinical implementation, ultimately promoting personalized cancer care and improving adaptive radiotherapy workflows.

Involved external institutions

Radboud University Nijmegen Medical Centre / Radboudumc of voluit Radboud Universitair Medisch Centrum (UMC) NL Netherlands (NL) University Medical Center Groningen (UMCG) / Universitair Medisch Centrum Groningen NL Netherlands (NL) Universitätsklinikum Köln DE Germany (DE) University Medical Centre Utrecht (UMC Utrecht) NL Netherlands (NL) Klinikum der Universität München (LMU Klinikum) DE Germany (DE) Skåne University Hospital / Skånes universitetssjukhus SE Sweden (SE) Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE)

How to cite

APA:

Thummerer, A., van der Bijl, E., Galapon, A.J., Kamp, F., Savenije, M., Muijs, C.,... Maspero, M. (2025). SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen. Medical Physics, 52(7). https://doi.org/10.1002/mp.17981

MLA:

Thummerer, Adrian, et al. "SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen." Medical Physics 52.7 (2025).

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