Journal Article

DKFZ-2025-01394

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen.

Thummerer, A. ; van der Bijl, E. ; Galapon, A. J. ; Kamp, F. ; Savenije, M. ; Muijs, C. ; Aluwini, S. ; Steenbakkers, R. J. H. M. ; Beuel, S. ; Intven, M. P. ; Langendijk, J. A. ; Both, S. ; Corradini, S. ; Rogowski, V. ; Terpstra, M. ; Wahl, N.DKFZ* ; Kurz, C. ; Landry, G.DKFZ* ; Maspero, M.

2025
Wiley Hoboken, NJ

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.

Keyword(s): Humans (MeSH) ; Image Processing, Computer-Assisted (MeSH) ; Magnetic Resonance Imaging (MeSH) ; Abdomen: diagnostic imaging (MeSH) ; Abdomen: radiation effects (MeSH) ; Head: diagnostic imaging (MeSH) ; Cone-Beam Computed Tomography (MeSH) ; Radiotherapy, Image-Guided (MeSH) ; Head and Neck Neoplasms: radiotherapy (MeSH) ; Head and Neck Neoplasms: diagnostic imaging (MeSH) ; Tomography, X-Ray Computed (MeSH) ; CBCT ; CT ; MR ; artificial intelligence ; deep learning ; image synthesis

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Contributing Institute(s):

1. E040 Med. Physik in der Strahlentherapie (E040)
2. DKTK Koordinierungsstelle München (MU01)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2025

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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