Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Huijben EM, Terpstra ML, Galapon AJ, Pai S, Thummerer A, Koopmans P, Afonso M, van Eijnatten M, Gurney-Champion O, Chen Z, Zhang Y, Zheng K, Li C, Pang H, Ye C, Wang R, Song T, Fan F, Qiu J, Huang Y, Ha J, Sung Park J, Alain-Beaudoin A, Bériault S, Yu P, Guo H, Huang Z, Li G, Zhang X, Fan Y, Liu H, Xin B, Nicolson A, Zhong L, Deng Z, Müller-Franzes G, Khader F, Li X, Zhang Y, Hémon C, Boussot V, Zhang Z, Wang L, Bai L, Wang S, Mus D, Kooiman B, Sargeant CA, Henderson EG, Kondo S, Kasai S, Karimzadeh R, Ibragimov B, Helfer T, Dafflon J, Chen Z, Wang E, Perko Z, Maspero M (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 97

Article Number: 103276

DOI: 10.1016/j.media.2024.103276

Abstract

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Authors with CRIS profile

Involved external institutions

Niigata University of Health and Welfare Japan (JP) Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia (AU) University of Copenhagen Denmark (DK) Keck School of Medicine of USC United States (USA) (US) Université de Rennes 1 / University of Rennes 1 France (FR) Universitätsklinikum Aachen (UKA) Germany (DE) Subtle Medical, Inc. United States (USA) (US) Southern Medical University / 南方医科大学 China (CN) Beijing Institute of Technology (BIT) / 北京理工大学 China (CN) University Medical Centre Utrecht (UMC Utrecht) Netherlands (NL) Eindhoven University of Technology / Technische Universiteit Eindhoven (TU/e) Netherlands (NL) University of Amsterdam Netherlands (NL) University Medical Center Groningen (UMCG) / Universitair Medisch Centrum Groningen Netherlands (NL) ShanghaiTech University / 上海科技大学 China (CN) Fudan University / 复旦大学 China (CN) Maastricht University Netherlands (NL) Radboud University Nijmegen Medical Centre / Radboudumc of voluit Radboud Universitair Medisch Centrum (UMC) Netherlands (NL) Indiana University United States (USA) (US) Wageningen University & Research Netherlands (NL) Elekta Sweden (SE) Beijing Infervision Technology China (CN) Shantou University (STU) / 汕头大学 China (CN) University of Manchester United Kingdom (GB) Vanderbilt University United States (USA) (US) Muroran Institute of Technology (MuIT) / 室蘭工業大学 Japan (JP) MRIguidance Netherlands (NL) State University of New York at Albany (UNY Albany / UAlbany) United States (USA) (US) Delft University of Technology (TU Delft) Netherlands (NL) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich Switzerland (CH) Medmind Technology Limited China (CN)

How to cite

APA:

Huijben, E.M., Terpstra, M.L., Galapon, A.J., Pai, S., Thummerer, A., Koopmans, P.,... Maspero, M. (2024). Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report. Medical Image Analysis, 97. https://doi.org/10.1016/j.media.2024.103276

MLA:

Huijben, Evi M.C., et al. "Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report." Medical Image Analysis 97 (2024).

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