%0 Journal Article
%A Thummerer, Adrian
%A van der Bijl, Erik
%A Galapon, Arthur Jr
%A Kamp, Florian
%A Savenije, Mark
%A Muijs, Christina
%A Aluwini, Shafak
%A Steenbakkers, Roel J H M
%A Beuel, Stephanie
%A Intven, Martijn Pw
%A Langendijk, Johannes A
%A Both, Stefan
%A Corradini, Stefanie
%A Rogowski, Viktor
%A Terpstra, Maarten
%A Wahl, Niklas
%A Kurz, Christopher
%A Landry, Guillaume
%A Maspero, Matteo
%T SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen.
%J Medical physics
%V 52
%N 7
%@ 0094-2405
%C Hoboken, NJ
%I Wiley
%M DKFZ-2025-01394
%P e17981
%D 2025
%X 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).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.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
%K Humans
%K Image Processing, Computer-Assisted
%K Magnetic Resonance Imaging
%K Abdomen: diagnostic imaging
%K Abdomen: radiation effects
%K Head: diagnostic imaging
%K Cone-Beam Computed Tomography
%K Radiotherapy, Image-Guided
%K Head and Neck Neoplasms: radiotherapy
%K Head and Neck Neoplasms: diagnostic imaging
%K Tomography, X-Ray Computed
%K CBCT (Other)
%K CT (Other)
%K MR (Other)
%K artificial intelligence (Other)
%K deep learning (Other)
%K image synthesis (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40665582
%R 10.1002/mp.17981
%U https://inrepo02.dkfz.de/record/302854