TY  - JOUR
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
TI  - SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen.
JO  - Medical physics
VL  - 52
IS  - 7
SN  - 0094-2405
CY  - Hoboken, NJ
PB  - Wiley
M1  - DKFZ-2025-01394
SP  - e17981
PY  - 2025
AB  - 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
KW  - Humans
KW  - Image Processing, Computer-Assisted
KW  - Magnetic Resonance Imaging
KW  - Abdomen: diagnostic imaging
KW  - Abdomen: radiation effects
KW  - Head: diagnostic imaging
KW  - Cone-Beam Computed Tomography
KW  - Radiotherapy, Image-Guided
KW  - Head and Neck Neoplasms: radiotherapy
KW  - Head and Neck Neoplasms: diagnostic imaging
KW  - Tomography, X-Ray Computed
KW  - CBCT (Other)
KW  - CT (Other)
KW  - MR (Other)
KW  - artificial intelligence (Other)
KW  - deep learning (Other)
KW  - image synthesis (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40665582
DO  - DOI:10.1002/mp.17981
UR  - https://inrepo02.dkfz.de/record/302854
ER  -