mlsanityData to 13 Oct 2026

SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy

arXiv 2502.17609 ↗First posted 24 Feb 2025physics.med-ph cs.AI cs.CVcited by 28

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Abstract (arXiv)

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.

Key facts. Cited by 28 papers in the map (data through 13 Oct 2026). Builds on 3 of its 30 references that are in the map. First posted on arXiv 24 Feb 2025.

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3 of 30 references.

References · 3

  1. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 603
  2. 2023SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapycited by 53
  3. 2021Deep learning-based synthetic-CT generation in radiotherapy and PET: a reviewcited by 28

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 9; 2026: 19.

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  1. 2025UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysiscited by 12
  2. 2025Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registrationcited by 4
  3. 2025Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCTcited by 2
  4. 2025Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesiscited by 2
  5. 2026Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridgecited by 2
  6. 2026DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapycited by 2
  7. 2026Generative Drifting for Conditional Medical Image Generationcited by 2
  8. 2026S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editingcited by 2
  9. 2025A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neckcited by 1
  10. 2025GANeXt: A Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesiscited by 1
  11. 2026Eliminating Registration Bias in Synthetic CT Generation: A Physics-Based Simulation Frameworkcited by 1
  12. 2026Cross Modality Image Translation In Medical Imaging Using Generative Frameworkscited by 1
16 more
  1. 2025Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Losscited by 0
  2. 2025DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCTcited by 0
  3. 2025MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generationcited by 0
  4. 2026A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesiscited by 0
  5. 2026Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planningcited by 0
  6. 2026VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translationcited by 0
  7. 2026MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Datasetcited by 0
  8. 2026SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignmentcited by 0
  9. 2026WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesiscited by 0
  10. 2026Spectral Consistent Flow for One-step 3D Medical Image Translationcited by 0
  11. 2026Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matchingcited by 0
  12. 2026MedUAG: Unified Understanding and Generation for Medical Multimodal Modelscited by 0
  13. 2026BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstructioncited by 0
  14. 2026TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCTcited by 0
  15. 2026When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generationcited by 0
  16. 2026RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathwaycited by 0