# SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy arXiv: https://arxiv.org/abs/2502.17609 ยท HTML: https://mlsanity.com/paper/2502.17609/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - Builds on 3 of its 30 references that are in the map. - First posted on arXiv 24 Feb 2025. ## Builds on (most cited first, 3 of 3) - [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022) - [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023) - [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021) ## Built on by (most cited first, 28 of 28) - [UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis](https://mlsanity.com/paper/2510.15710/) (2025) - [Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration](https://mlsanity.com/paper/2510.21358/) (2025) - [Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT](https://mlsanity.com/paper/2509.21913/) (2025) - [Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis](https://mlsanity.com/paper/2510.04823/) (2025) - [Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge](https://mlsanity.com/paper/2602.16664/) (2026) - [DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy](https://mlsanity.com/paper/2604.12778/) (2026) - [Generative Drifting for Conditional Medical Image Generation](https://mlsanity.com/paper/2604.19736/) (2026) - [S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing](https://mlsanity.com/paper/2606.24441/) (2026) - [A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck](https://mlsanity.com/paper/2506.08654/) (2025) - [GANeXt: A Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesis](https://mlsanity.com/paper/2512.19336/) (2025) - [Eliminating Registration Bias in Synthetic CT Generation: A Physics-Based Simulation Framework](https://mlsanity.com/paper/2602.02130/) (2026) - [Cross Modality Image Translation In Medical Imaging Using Generative Frameworks](https://mlsanity.com/paper/2605.13686/) (2026) - [Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss](https://mlsanity.com/paper/2509.22394/) (2025) - [DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT](https://mlsanity.com/paper/2511.12098/) (2025) - [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025) - [A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis](https://mlsanity.com/paper/2603.13520/) (2026) - [Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning](https://mlsanity.com/paper/2603.23295/) (2026) - [VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation](https://mlsanity.com/paper/2604.22942/) (2026) - [MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset](https://mlsanity.com/paper/2604.27654/) (2026) - [SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment](https://mlsanity.com/paper/2605.08724/) (2026) - [WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis](https://mlsanity.com/paper/2607.06234/) (2026) - [Spectral Consistent Flow for One-step 3D Medical Image Translation](https://mlsanity.com/paper/2607.10627/) (2026) - [Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching](https://mlsanity.com/paper/2608.08135/) (2026) - [MedUAG: Unified Understanding and Generation for Medical Multimodal Models](https://mlsanity.com/paper/2608.18937/) (2026) - [BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction](https://mlsanity.com/paper/2609.04747/) (2026) - [TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCT](https://mlsanity.com/paper/2609.13838/) (2026) - [When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation](https://mlsanity.com/paper/2609.29387/) (2026) - [RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway](https://mlsanity.com/paper/2610.06923/) (2026) ## 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.