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Abstract
The purpose of this study is to evaluate the performance of a recurrent neural network (RNN)-based prediction algorithm to compensate for respiratory movement using an articulated robotic couch system. A prototype of a real-time respiratory motion compensation couch was built using an optical 3D motion tracking system and a six-degree-of-freedom-articulated robotic system. To compensate for the system latency from motion detection to re-positioning of the system, RNN and double exponential smoothing (ES2) prediction algorithms were applied. Three aspects of performance were evaluated, simulation and experiments for geometric and dosimetric evaluations, using data from three liver and three lung patients who underwent stereotactic body radiotherapy. Overall, the RNN algorithm showed better geometric and dosimetric results than the other approaches. In simulation tests, RNN showed 82% average improvement ratio, compared with non-predicted results. In the geometric evaluation, RNN only showed average FWHM broadening of 1.5 mm, compared with the static case. In the dosimetric evaluation, RNN showed average gamma passing rates of 97.4 ± 1.0%, 89.0 ± 2.4% under the 3%/3 mm, 2%/2 mm respectively. It may be technically feasible to use the RNN prediction algorithm to compensate for respiratory motion with an articulated robotic couch system. The RNN algorithm could be widely used for motion compensation in patients undergoing radiotherapy.

Affiliations

Minsik Lee, Min-Seok Cho, Chiyoung Jeong, Jungwon Kwak, Jinhong Jung, Su Ssan Kim, Sang Min Yoon, Si Yeol Song, Sang-wook Lee, Jong Hoon Kim, Eun Kyung Choi & Byungchul Cho
Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Korea

Hoyeon Lee & Seungryong Cho
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Eoeun-dong, Yuseong-gu, Daejeon, Korea