Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.
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Huijben Evi M C, Terpstra Maarten L, Galapon Arthur Jr, Pai Suraj, Thummerer Adrian, Koopmans Peter, Afonso Manya, van Eijnatten Maureen, Gurney-Champion Oliver, Chen Zeli, Zhang Yiwen, Zheng Kaiyi, Li Chuanpu, Pang Haowen, Ye Chuyang, Wang Runqi, Song Tao, Fan Fuxin, Qiu Jingna, Huang Yixing, Ha Juhyung, Sung Park Jong, Alain-Beaudoin Alexandra, Bériault Silvain, Yu Pengxin, Guo Hongbin, Huang Zhanyao, Li Gengwan, Zhang Xueru, Fan Yubo, Liu Han, Xin Bowen, Nicolson Aaron, Zhong Lujia, Deng Zhiwei, Müller-Franzes Gustav, Khader Firas, Li Xia, Zhang Ye, Hémon Cédric, Boussot Valentin, Zhang Zhihao, Wang Long, Bai Lu, Wang Shaobin, Mus Derk, Kooiman Bram, Sargeant Chelsea A H, Henderson Edward G A, Kondo Satoshi, Kasai Satoshi, Karimzadeh Reza, Ibragimov Bulat, Helfer Thomas, Dafflon Jessica, Chen Zijie, Wang Enpei, Perko Zoltan, Maspero Matteo
Аннотация
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.