# SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy arXiv: https://arxiv.org/abs/2303.16320 ยท HTML: https://mlsanity.com/paper/2303.16320/ ## Key facts - Cited by 53 papers in the map (data through 14 Oct 2026). - Builds on 1 of its 11 references that are in the map. - First posted on arXiv 28 Mar 2023. ## Builds on (most cited first, 1 of 1) - [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, 30 of 53) - [HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation](https://mlsanity.com/paper/2502.09838/) (2025) - [SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy](https://mlsanity.com/paper/2502.17609/) (2025) - [3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation](https://mlsanity.com/paper/2312.15676/) (2023) - [MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation](https://mlsanity.com/paper/2310.02663/) (2023) - [MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration](https://mlsanity.com/paper/2401.13934/) (2024) - [Soft Masked Mamba Diffusion Model for CT to MRI Conversion](https://mlsanity.com/paper/2406.15910/) (2024) - [SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation](https://mlsanity.com/paper/2305.15367/) (2023) - [FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model](https://mlsanity.com/paper/2311.12070/) (2023) - [VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module](https://mlsanity.com/paper/2404.05105/) (2024) - [Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review](https://mlsanity.com/paper/2403.18565/) (2024) - [Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs](https://mlsanity.com/paper/2407.14994/) (2024) - [Anatomical feature-prioritized loss for enhanced MR to CT translation](https://mlsanity.com/paper/2410.10328/) (2024) - [Multi-resolution Guided 3D GANs for Medical Image Translation](https://mlsanity.com/paper/2412.00575/) (2024) - [Large Language Models for Multimodal Deformable Image Registration](https://mlsanity.com/paper/2408.10703/) (2024) - [Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review](https://mlsanity.com/paper/2509.17790/) (2025) - [Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions](https://mlsanity.com/paper/2511.01284/) (2025) - [Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning](https://mlsanity.com/paper/2407.07660/) (2024) - [seg2med: a bridge from artificial anatomy to multimodal medical images](https://mlsanity.com/paper/2504.09182/) (2025) - [Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching](https://mlsanity.com/paper/2601.15884/) (2026) - [Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge](https://mlsanity.com/paper/2602.16664/) (2026) - [MRI-to-CT synthesis using drifting models](https://mlsanity.com/paper/2603.28498/) (2026) - [Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion](https://mlsanity.com/paper/2409.05982/) (2024) - [Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation](https://mlsanity.com/paper/2410.13427/) (2024) - [Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds](https://mlsanity.com/paper/2503.08515/) (2025) - [FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesis](https://mlsanity.com/paper/2505.07687/) (2025) - [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025) - [Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment](https://mlsanity.com/paper/2506.08716/) (2025) - [UniFuse: A Unified All-in-One Framework for Multi-Modal Medical Image Fusion Under Diverse Degradations and Misalignments](https://mlsanity.com/paper/2506.22736/) (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) ## Abstract (arXiv) Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and validation methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in CSV files provided with the datasets. Data format and usage notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.