Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
Oct 1, 2024·
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0 min read
Evi M.C. Huijben
Maarten L. Terpstra
Arthur Jr. Galapon
Suraj Pai
Adrian Thummerer
Peter Koopmans
Manya Afonso
Maureen Van Eijnatten
Oliver Gurney-Champion
Zeli Chen
Yiwen Zhang
Kaiyi Zheng
Chuanpu Li
Haowen Pang
Chuyang Ye
Runqi Wang
Tao Song
Fuxin Fan
Jingna Qiu
Yixing Huang
Juhyung Ha
Jong Sung Park
Alexandra Alain-Beaudoin
Silvain Bériault
Pengxin Yu
Hongbin Guo
Zhanyao Huang
Gengwan Li
Xueru Zhang
Yubo Fan
Han Liu
Bowen Xin
Aaron Nicolson
Lujia Zhong
Zhiwei Deng
Gustav Müller-Franzes
Firas Khader
Xia Li
Ye Zhang
Cédric Hémon
Valentin Boussot
Zhihao Zhang
Long Wang
Lu Bai
Shaobin Wang
Derk Mus
Bram Kooiman
C.A. Sargeant
E.G. Henderson
Shinya Kondo
Shunji Kasai
Reza Karimzadeh
Boris Ibragimov
Thomas Helfer
Julien Dafflon
E. Wang
Zoltan Perko
Matteo Maspero
Abstract
SynthRAD2023 challenge aimed to compare synthetic CT generation methods using real-world multi-center data from 1080 patients, divided into two tasks: MRI-to-CT and CBCT-to-CT. Evaluation included image similarity metrics and dose-based metrics for both proton and photon planning. The challenge attracted 617 registrants, with 22 and 17 valid submissions respectively. The best-performing teams achieved high scores in structural similarity index and gamma pass rate. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the necessity of dose evaluation when assessing the clinical applicability of synthetic CTs. SynthRAD2023 facilitated research and benchmarking of synthetic CT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy.
Type
Publication
Medical Image Analysis
