Needle Trajectory Prediction for Percutaneous Kidney Biopsy in 5G-Powered Teleultrasound Navigation System

Lei Zhao, Guanghua Tan, Jiewen Lai, Chwee Ming Lim, Weng Kin Wong, Hongliang Ren, Kenli Li

Abstract: Needle insertion is a critical component of many remote surgical procedures, including biopsies, injections, neurosurgery, and brachytherapy cancer treatments. However, precise visualization of the biopsy needle trajectory remains challenging due to specular reflection, speckle noise, and needle-like anatomical features. This paper proposes a visual feedback prediction framework for ultrasound-assisted percutaneous kidney biopsy in 5G remote surgery, aiming to enhance operator confidence, reduce procedure time, and minimize the risk of unintended bleeding. Building upon this framework, we design a Lightweight-Accuracy Needle Trajectory Prediction (LA-NTP) model by minimizing the backbone and optimizing the multi-module prediction process, incorporating innovative training strategies (i.e., angle-aware geometric and trajectory augmentation losses). The experimental results demonstrate that it achieves competitive performance with only 20.3% of the model size of the previous best real-time method and a 3.7-fold increase in inference speed. Even in challenging scenarios involving large insertion depths and steep angles, our method provides stable and precise navigation.