A review of artificial intelligence applications for motion tracking in radiotherapy. Issue 5 (19th July 2021)

• Record Type: Journal Article
• Authors: Mylonas, Adam; Booth, Jeremy; Nguyen, Doan Trang
• Is Part Of: Journal of medical imaging and radiation oncology. Volume 65:Issue 5(2021)
• Page Start: 596
• Page End: 611
• Publication Date: 2021-07-19
• Subjects: artificial intelligence -- deep learning -- machine learning -- motion tracking -- radiation oncology; Radiology, Medical -- Periodicals; Radiology, Medical -- Australasia -- Periodicals; 616.0757
• DOI: 10.1111/1754-9485.13285
• Languages: English
• ISSNs: 1754-9477

Abstract

Abstract: During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real‐time image‐guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)‐based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker‐based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker‐based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.