Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

doi:10.1016/j.media.2024.103276

	Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report
	Huijben, Evi M.C.1; Terpstra, Maarten L.2,3; Galapon, Arthur Jr.4; Pai, Suraj 5; Thummerer, Adrian 4,6; Koopmans, Peter 7; Afonso, Manya 8; van Eijnatten, Maureen 1; Gurney-Champion, Oliver 9,10; Chen, Zeli 11; Zhang, Yiwen 11; Zheng, Kaiyi 11; Li, Chuanpu 11; Pang, Haowen 12; Ye, Chuyang 12; Wang, Runqi 13; Song, Tao 14; Fan, Fuxin 15; Qiu, Jingna 15; Huang, Yixing 15; Ha, Juhyung 16; Sung Park, Jong 16; Alain-Beaudoin, Alexandra 17; Bériault, Silvain 17; Yu, Pengxin 18; Guo, Hongbin 19; Huang, Zhanyao 19; Li, Gengwan 20; Zhang, Xueru 20; Fan, Yubo 21; Liu, Han 21; Xin, Bowen 22; Nicolson, Aaron 22; Zhong, Lujia 23; Deng, Zhiwei 23; Müller-Franzes, Gustav 24; Khader, Firas 24; Li, Xia 25; Zhang, Ye 25; Hémon, Cédric 26; Boussot, Valentin 26; Zhang, Zhihao 27; Wang, Long 27; Bai, Lu 28; Wang, Shaobin 28; Mus, Derk 29; Kooiman, Bram 29; Sargeant, Chelsea A.H.30; Henderson, Edward G.A.30; Kondo, Satoshi 31; Kasai, Satoshi 32; Karimzadeh, Reza 33; Ibragimov, Bulat 33; Helfer, Thomas 34; Dafflon, Jessica 35,36; Chen, Zijie 37; Wang, Enpei 37; Perko, Zoltan 38; Maspero, Matteo 2,3
	2024-10
发表期刊	MEDICAL IMAGE ANALYSIS (IF:10.7[JCR-2023],11.9[5-Year])
ISSN	1361-8415
EISSN	1361-8423
卷号	97
发表状态	已发表
DOI	10.1016/j.media.2024.103276
摘要	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. © 2024 The Author(s)
关键词	Carrier concentration Computerized tomography Deep learning Electron density measurement Electrons Image analysis Magnetic resonance imaging Medical imaging Photons Tissue Adaptive radiotherapy Cone-beam CT Deep learning Generation techniques Healthy tissues Image similarity Images synthesis Medical image synthesis Synthetic computed tomography generation Treatment planning
URL	查看原文
收录类别	EI ; SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001285421500001
出版者	Elsevier B.V.
EI入藏号	20243116772683
EI主题词	Radiotherapy
EI分类号	461.1 Biomedical Engineering ; 461.2 Biological Materials and Tissue Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 622.3 Radioactive Material Applications ; 701.1 Electricity: Basic Concepts and Phenomena ; 701.2 Magnetism: Basic Concepts and Phenomena ; 723.5 Computer Applications ; 746 Imaging Techniques ; 931.3 Atomic and Molecular Physics
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/407242
专题	生物医学工程学院
通讯作者	Maspero, Matteo
作者单位	1.Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; 2.Radiotherapy Department, University Medical Center Utrecht, Utrecht, Netherlands; 3.Computational Imaging Group for MR Diagnostics & Therapy, University Medical Center Utrecht, Utrecht, Netherlands; 4.Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands; 5.Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, Netherlands; 6.Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; 7.Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, Netherlands; 8.Wageningen University & Research, Wageningen Plant Research, Wageningen, Netherlands; 9.Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, Netherlands; 10.Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, Netherlands; 11.School of Biomedical Engineering, Southern Medical University, Guangzhou, China; 12.School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China; 13.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; 14.Fudan University, Shanghai, China; 15.Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; 16.Indiana University, Bloomington, United States; 17.Advanced Development Engineering, Elekta Ltd, Montreal, Canada; 18.Infervision Medical Technology Co., Ltd. Beijing, China; 19.Department of Biomedical Engineering, Shantou University, China; 20.Independent researchers; 21.Department of Computer Science, Vanderbilt University, Nashville, United States; 22.Australian e-Health Research Centre, CSIRO, Herston; QLD, Australia; 23.Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles; CA, United States; 24.University Hospital Aachen, Aachen, Germany; 25.Center for Proton Therapy, Paul Scherrer Institut, Villigen, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland; 26.University Rennes 1, CLCC Eugène Marquis, INSERM, LTSI, Rennes, France; 27.Subtle Medical, Shanghai, China; 28.MedMind Technology Co. Ltd., Beijing, China; 29.MRI Guidance BV, Utrecht, Netherlands; 30.Division of Cancer Sciences, The University of Manchester, United Kingdom; 31.Muroran Institute of Technology, Hokkaido, Japan; 32.Niigata University of Health and Welfare, Niigata, Japan; 33.Image Analysis, Computational Modelling and Geometry, University of Copenhagen, Denmark; 34.IACS, Stony Brook University, NY, United States; 35.Data Science and Sharing Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, United States; 36.Machine Learning Team, Functional Magnetic Resonance Imaging Facility National Institute of Mental Health, Bethesda, United States; 37.Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China; 38.Delft University of Technology, Faculty of Applied Sciences, Department of Radiation Science and Technology, Delft, Netherlands
推荐引用方式 GB/T 7714	Huijben, Evi M.C.,Terpstra, Maarten L.,Galapon, Arthur Jr.,et al. Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report[J]. MEDICAL IMAGE ANALYSIS,2024,97.
APA	Huijben, Evi M.C..,Terpstra, Maarten L..,Galapon, Arthur Jr..,Pai, Suraj.,Thummerer, Adrian.,...&Maspero, Matteo.(2024).Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.MEDICAL IMAGE ANALYSIS,97.
MLA	Huijben, Evi M.C.,et al."Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report".MEDICAL IMAGE ANALYSIS 97(2024).