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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2502.17609 |
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| _version_ | 1866918097902895104 |
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| author | Thummerer, Adrian van der Bijl, Erik Galapon, Arthur Jr Kamp, Florian Savenije, Mark Muijs, Christina Aluwini, Shafak Steenbakkers, Roel J. H. M. Beuel, Stephanie Intven, Martijn P. W. Langendijk, Johannes A. Both, Stefan Corradini, Stefanie Rogowski, Viktor Terpstra, Maarten Wahl, Niklas Kurz, Christopher Landry, Guillaume Maspero, Matteo |
| author_facet | Thummerer, Adrian van der Bijl, Erik Galapon, Arthur Jr Kamp, Florian Savenije, Mark Muijs, Christina Aluwini, Shafak Steenbakkers, Roel J. H. M. Beuel, Stephanie Intven, Martijn P. W. Langendijk, Johannes A. Both, Stefan Corradini, Stefanie Rogowski, Viktor Terpstra, Maarten Wahl, Niklas Kurz, Christopher Landry, Guillaume Maspero, Matteo |
| contents | Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT), is gaining traction in radiotherapy. The SynthRAD2025 dataset and Grand Challenge promote advancements in sCT generation by providing a benchmarking platform for algorithms using cone-beam CT (CBCT) and magnetic resonance imaging (MRI).
The dataset includes 2362 cases: 890 MRI-CT and 1472 CBCT-CT pairs from head-and-neck, thoracic, and abdominal cancer patients treated at five European university medical centers (UMC Groningen, UMC Utrecht, Radboud UMC, LMU University Hospital Munich, and University Hospital of Cologne). Data were acquired with diverse scanners and protocols. Pre-processing, including rigid and deformable image registration, ensures high-quality, modality-aligned images. Extensive quality assurance validates image consistency and usability.
All imaging data is provided in MetaImage (.mha) format, ensuring compatibility with medical image processing tools. Metadata, including acquisition parameters and registration details, is available in structured CSV files. To maintain dataset integrity, SynthRAD2025 is divided into training (65%), validation (10%), and test (25%) sets. The dataset is accessible at https://doi.org/10.5281/zenodo.14918089 under the SynthRAD2025 collection.
This dataset supports benchmarking and the development of synthetic imaging techniques for radiotherapy applications. Use cases include sCT generation for MRI-only and MR-guided photon/proton therapy, CBCT-based dose calculations, and adaptive radiotherapy workflows. By integrating diverse acquisition settings, SynthRAD2025 fosters robust, generalizable image synthesis algorithms, advancing personalized cancer care and adaptive radiotherapy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy Thummerer, Adrian van der Bijl, Erik Galapon, Arthur Jr Kamp, Florian Savenije, Mark Muijs, Christina Aluwini, Shafak Steenbakkers, Roel J. H. M. Beuel, Stephanie Intven, Martijn P. W. Langendijk, Johannes A. Both, Stefan Corradini, Stefanie Rogowski, Viktor Terpstra, Maarten Wahl, Niklas Kurz, Christopher Landry, Guillaume Maspero, Matteo Medical Physics Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT), is gaining traction in radiotherapy. The SynthRAD2025 dataset and Grand Challenge promote advancements in sCT generation by providing a benchmarking platform for algorithms using cone-beam CT (CBCT) and magnetic resonance imaging (MRI). The dataset includes 2362 cases: 890 MRI-CT and 1472 CBCT-CT pairs from head-and-neck, thoracic, and abdominal cancer patients treated at five European university medical centers (UMC Groningen, UMC Utrecht, Radboud UMC, LMU University Hospital Munich, and University Hospital of Cologne). Data were acquired with diverse scanners and protocols. Pre-processing, including rigid and deformable image registration, ensures high-quality, modality-aligned images. Extensive quality assurance validates image consistency and usability. All imaging data is provided in MetaImage (.mha) format, ensuring compatibility with medical image processing tools. Metadata, including acquisition parameters and registration details, is available in structured CSV files. To maintain dataset integrity, SynthRAD2025 is divided into training (65%), validation (10%), and test (25%) sets. The dataset is accessible at https://doi.org/10.5281/zenodo.14918089 under the SynthRAD2025 collection. This dataset supports benchmarking and the development of synthetic imaging techniques for radiotherapy applications. Use cases include sCT generation for MRI-only and MR-guided photon/proton therapy, CBCT-based dose calculations, and adaptive radiotherapy workflows. By integrating diverse acquisition settings, SynthRAD2025 fosters robust, generalizable image synthesis algorithms, advancing personalized cancer care and adaptive radiotherapy. |
| title | SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy |
| topic | Medical Physics Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2502.17609 |