Abstract
Background
In the needle biopsy, the respiratory motion causes the displacement of thoracic‐abdominal soft tissues, which brings great difficulty to accurate localization. Based on internal target motion and external marker motion, existing methods need to establish a correlation model or a prediction model to compensate the respiratory movement, which can hardly achieve required accuracy in clinic use due to the complexity of the internal tissue motion.
Methods
In order to improve tracking accuracy and reduce the number of models, we propose a framework for target localization based on long short‐term memory (LSTM) method. Combined with the correlation model and the prediction model by using LSTM, we adopted the principal component of time series features of external surrogate signals to predict the trajectory of the internal tumor target. Additionally, based on the electromagnetic tracking system (EMT) and Universal Robots 3 (UR3) robotic arm, we applied the proposed approach to a prototype of robotic puncture system for real time tumor tracking.
Results
To verify the proposed method, experiments on both public datasets and customized motion phantom for respiratory simulation were performed. In the public dataset study, an average mean absolute error (MAE), and an average root mean square error (RMSE) of predictive results of 0.44 mm and 0.58 mm were achieved respectively. In the motion phantom study, an average root mean square (RMS) of puncturing error resulted in 0.65mm.
Conclusion
The experimental results demonstrate the proposed method improves the accuracy of target localization during respiratory movement and appeals the potentials applying to clinical application.
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