TY - JOUR
T1 - SynthRAD2025 Grand Challenge dataset
T2 - Generating synthetic CTs for radiotherapy from head to abdomen
AU - Thummerer, Adrian
AU - van der Bijl, Erik
AU - Galapon, Arthur Jr
AU - Kamp, Florian
AU - Savenije, Mark
AU - Muijs, Christina
AU - Aluwini, Shafak
AU - Steenbakkers, Roel J H M
AU - Beuel, Stephanie
AU - Intven, Martijn Pw
AU - Langendijk, Johannes A
AU - Both, Stefan
AU - Corradini, Stefanie
AU - Rogowski, Viktor
AU - Terpstra, Maarten
AU - Wahl, Niklas
AU - Kurz, Christopher
AU - Landry, Guillaume
AU - Maspero, Matteo
N1 - © 2025 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.
PY - 2025/7/15
Y1 - 2025/7/15
N2 - 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.
AB - 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.
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Magnetic Resonance Imaging
KW - Abdomen/diagnostic imaging
KW - Head/diagnostic imaging
KW - Cone-Beam Computed Tomography
KW - Radiotherapy, Image-Guided
KW - Head and Neck Neoplasms/radiotherapy
KW - Tomography, X-Ray Computed
U2 - 10.1002/mp.17981
DO - 10.1002/mp.17981
M3 - Article
C2 - 40665582
SN - 0094-2405
VL - 52
JO - Medical Physics
JF - Medical Physics
IS - 7
M1 - e17981
ER -