Real-time respiratory motion prediction using photonic reservoir computing

• Published in: Scientific reports Vol. 13; no. 1; pp. 5718 - 12
• Main Authors: Liang, Zhizhuo, Zhang, Meng, Shi, Chengyu, Huang, Z. Rena
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.04.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1059/485; 639/624/1075/401; 639/705/117; Algorithms; Humanities and Social Sciences; Humans; Lung cancer; Lung Neoplasms - radiotherapy; Mean square errors; Motion; Movement; multidisciplinary; Neural networks; Neural Networks, Computer; Predictions; Radiation therapy; Respiration; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-31296-2

Respiration induced motion is a well-recognized challenge in many clinical practices including upper body imaging, lung tumor motion tracking and radiation therapy. In this work, we present a recurrent neural network algorithm that was implemented in a photonic delay-line reservoir computer (RC) for real-time respiratory motion prediction. The respiratory motion signals are quasi-periodic waveforms subject to a variety of non-linear distortions. In this work, we demonstrated for the first time that RC can be effective in predicting short to medium range of respiratory motions within practical timescales. A double-sliding window technology is explored to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed respiratory motion data. A breathing dataset from a total of 76 patients with breathing speeds ranging from 3 to 20 breaths per minute (BPM) is studied. Motion prediction of look-ahead times of 66.6, 166.6, and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.025, an average mean absolute error (MAE) of 0.34 mm, an average root mean square error (RMSE) of 0.45 mm, an average therapeutic beam efficiency (TBE) of 94.14% for an absolute error (AE) < 1 mm, and 99.89% for AE < 3 mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.