Artificial Intelligence in Radiation Therapy

被引:9
|
作者
Fu, Yabo [1 ,2 ]
Zhang, Hao [3 ]
Morris, Eric D. [4 ]
Glide-Hurst, Carri K. [5 ]
Pai, Suraj [6 ]
Traverso, Alberto [6 ]
Wee, Leonard [6 ]
Hadzic, Ibrahim [6 ]
Lonne, Per-Ivar [7 ]
Shen, Chenyang [8 ]
Liu, Tian [1 ,2 ]
Yang, Xiaofeng [1 ,2 ]
机构
[1] Emory Univ, Dept Radiat Oncol, Atlanta, GA 30322 USA
[2] Emory Univ, Winship Canc Inst, Atlanta, GA 30322 USA
[3] Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10065 USA
[4] Univ Calif Los Angeles, Dept Radiat Oncol, Los Angeles, CA 90095 USA
[5] Univ Wisconsin Madison, Sch Med & Publ Hlth, Dept Human Oncol, Madison, WI 53792 USA
[6] Maastricht Univ, Dept Radiotherapy MAASTRO, Med Ctr, NL-6221 XE Maastricht, Netherlands
[7] Oslo Univ Hosp, Dept Med Phys, N-0424 Oslo, Norway
[8] Univ Texas Southwestern Med Ctr Dallas, Dept Radiat Oncol, Dallas, TX 75002 USA
基金
荷兰研究理事会;
关键词
Artificial intelligence (AI); image reconstruction; image registration; image segmentation; image synthesis; radiotherapy; treatment planning; DEFORMABLE IMAGE REGISTRATION; MODULATED ARC THERAPY; HEAD-AND-NECK; KNOWLEDGE-BASED PREDICTION; BEAM COMPUTED-TOMOGRAPHY; ANATOMIC CHANGES; NEURAL-NETWORK; PLAN QUALITY; MULTICRITERIA OPTIMIZATION; AUTOMATIC SEGMENTATION;
D O I
10.1109/TRPMS.2021.3107454
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
引用
收藏
页码:158 / 181
页数:24
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