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@ARTICLE{Thummerer:302854,
      author       = {A. Thummerer and E. van der Bijl and A. J. Galapon and F.
                      Kamp and M. Savenije and C. Muijs and S. Aluwini and R. J.
                      H. M. Steenbakkers and S. Beuel and M. P. Intven and J. A.
                      Langendijk and S. Both and S. Corradini and V. Rogowski and
                      M. Terpstra and N. Wahl$^*$ and C. Kurz and G. Landry$^*$
                      and M. Maspero},
      title        = {{S}ynth{RAD}2025 {G}rand {C}hallenge dataset: {G}enerating
                      synthetic {CT}s for radiotherapy from head to abdomen.},
      journal      = {Medical physics},
      volume       = {52},
      number       = {7},
      issn         = {0094-2405},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-01394},
      pages        = {e17981},
      year         = {2025},
      abstract     = {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 $(65\%),$ validation $(10\%),$ and test
                      $(25\%)$ sets. The dataset is accessible through
                      https://doi.org/10.5281/zenodo.14918088 under the
                      SynthRAD2025 collection.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.},
      keywords     = {Humans / Image Processing, Computer-Assisted / Magnetic
                      Resonance Imaging / Abdomen: diagnostic imaging / Abdomen:
                      radiation effects / Head: diagnostic imaging / Cone-Beam
                      Computed Tomography / Radiotherapy, Image-Guided / Head and
                      Neck Neoplasms: radiotherapy / Head and Neck Neoplasms:
                      diagnostic imaging / Tomography, X-Ray Computed / CBCT
                      (Other) / CT (Other) / MR (Other) / artificial intelligence
                      (Other) / deep learning (Other) / image synthesis (Other)},
      cin          = {E040 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40665582},
      doi          = {10.1002/mp.17981},
      url          = {https://inrepo02.dkfz.de/record/302854},
}