TY - JOUR
T1 - Artificial Intelligence in Radiation Therapy
AU - Fu, Yabo
AU - Zhang, Hao
AU - Morris, Eric D.
AU - Glide-Hurst, Carri K.
AU - Pai, Suraj
AU - Traverso, Alberto
AU - Wee, Leonard
AU - Hadzic, Ibrahim
AU - Lonne, Per Ivar
AU - Shen, Chenyang
AU - Liu, Tian
AU - Yang, Xiaofeng
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - Artificial intelligence (AI)
KW - image reconstruction
KW - image registration
KW - image segmentation
KW - image synthesis
KW - radiotherapy
KW - treatment planning
UR - http://www.scopus.com/inward/record.url?scp=85113826607&partnerID=8YFLogxK
U2 - 10.1109/TRPMS.2021.3107454
DO - 10.1109/TRPMS.2021.3107454
M3 - Article
C2 - 35992632
AN - SCOPUS:85113826607
SN - 2469-7311
VL - 6
SP - 158
EP - 181
JO - IEEE Transactions on Radiation and Plasma Medical Sciences
JF - IEEE Transactions on Radiation and Plasma Medical Sciences
IS - 2
ER -