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 p...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.展开更多
基金supported in part by the National Key R&D Program of China under grant 2022YFC240014102the Beijing Municipal Natural Science Foundation-Haidian Primitive Innovation Joint Fund Project under grant L212005the National Natural Science Foundation of China under grants 62073043 and 62003045.
文摘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.
