A Respiratory Motion Prediction Method Based on LSTM-AE with Attention Mechanism for Spine Surgery
Zhe Han, Huanyu Tian, Xiaoguang Han, Jiayuan Wu, Weijun Zhang, Changsheng Li, Liang Qiu, Xingguang Duan, Wei Tian
Abstract
Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery, resulting in inaccurate positional information of the target region and unexpected damage during the operation. In this paper, we propose a novel deep learning architecture for respiratory motion prediction, which can adapt to different patients. The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation. To ensure real-time performance, a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced. The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method. The experimental results demonstrate that the presented method (RMSE: 4.39%) outperforms other methods in terms of accuracy within a learning time of 2 min. The maximum predictive errors under the latency of 333 ms with respect to the x , y , and z axes of the optical camera system were 0.13, 0.07, and 0.10 mm, respectively, within a motion range of 2 mm.