生成用于放射治疗的合成计算机断层扫描：SynthRAD2023 挑战报告。
Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.
发表日期：2024 Jul 17
作者：
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, Chelsea A H Sargeant, Edward G A Henderson, Satoshi Kondo, Satoshi Kasai, Reza Karimzadeh, Bulat Ibragimov, Thomas Helfer, Jessica Dafflon, Zijie Chen, Enpei Wang, Zoltan Perko, Matteo Maspero
来源：
MEDICAL IMAGE ANALYSIS
摘要：
放射治疗在癌症治疗中发挥着至关重要的作用，需要在多天内向肿瘤精确输送放射线，同时保护健康组织。计算机断层扫描 (CT) 是治疗计划不可或缺的一部分，它提供对于精确剂量计算至关重要的电子密度数据。然而，准确呈现患者解剖结构具有挑战性，尤其是在适应性放射治疗中，CT 并非每天采集。磁共振成像 (MRI) 提供卓越的软组织对比度。尽管如此，它仍然缺乏电子密度信息，而锥形束CT（CBCT）缺乏直接的电子密度校准，主要用于患者定位。采用仅 MRI 或基于 CBCT 的适应性放射治疗消除了 CT 计划的需要，但也带来了挑战。合成 CT (sCT) 生成技术旨在通过使用图像合成弥合 MRI、CBCT 和 CT 之间的差距来应对这些挑战。 SynthRAD2023 挑战赛的组织目的是使用来自 1080 名患者的多中心真实数据来比较合成 CT 生成方法，分为两个任务：(1) MRI 到 CT 和 (2) CBCT 到 CT。评估包括图像相似性以及质子和光子计划中基于剂量的指标。该挑战吸引了大量参与，共有 617 名报名者和 22/17 的任务 1/2 有效提交。表现最好的团队实现了光子计划的高结构相似性指数（≥0.87/0.90）和伽马通过率（≥98.1%/99.0%）和质子计划（≥97.3%/97.0%）。然而，图像相似性指标和剂量准确性之间没有发现显着相关性，这强调了在评估 sCT 的临床适用性时需要进行剂量评估。 SynthRAD2023 促进了 sCT 生成技术的调查和基准测试，为开发仅 MRI 和基于 CBCT 的适应性放射治疗提供了见解。它展示了深度学习生成高质量 sCT 的能力不断增强，从而减少了治疗计划对传统 CT 的依赖。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。
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.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.