AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery

Tianye Niu; Tiffany Tsui; Wei Zhao

doi:10.1016/j.semradonc.2022.06.006

AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery

Semin Radiat Oncol. 2022 Oct;32(4):365-376. doi: 10.1016/j.semradonc.2022.06.006.

Authors

Tianye Niu¹, Tiffany Tsui², Wei Zhao³

Affiliations

¹ Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China. Electronic address: niuty@szbl.ac.cn.
² Nuclear and Radiological Engineering and Medical Physics, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
³ Department of Medical Physics, School of Physics, Beihang University, Beijing, P.R. China.

PMID: 36202439
DOI: 10.1016/j.semradonc.2022.06.006

Abstract

Cancer accounts for nearly 10 million deaths in 2020 and is a leading cause of death worldwide. Radiation therapy is an effective modality to cure cancer. The ultimate goal of successful radiation therapy is to accurately deliver a safe and therapeutic dose of radiation to cancerous target cells while limiting radiation to the healthy tissue in the beam pathway and the surrounding normal tissue. With advancing radiation treatment technology, better imaging techniques are imperative for safe, high-dose delivery. Artificial intelligence developments on image-guided radiation therapy technologies, which range from Kilovoltage and Megavoltage modalities to two-dimensional and three-dimensional techniques, are discussed in depth.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Humans
Neoplasms* / diagnostic imaging
Neoplasms* / radiotherapy
Radiotherapy, Image-Guided* / methods
X-Rays