Real-Time Robotic Needle Insertion in Deformable and Moving Structure Using Learning-By-Example Method

Abstract

This paper presents an innovative and practical method for robotic needle steering in radio-frequency ablation (RFA) to treat cancer. One of the main challenges in this process is that tissue shifts and deforms during needle insertion, making it difficult to accurately predict the needle's path in real time. Inverse finite element (iFE) simulations have been used to address this problem. While these methods are accurate, they often require further refinement for effective time performance in real-world robotic systems. This is because when the method is incorporated into a real robot, there can be a delay in command execution. To address this challenge, we propose a machine learning-based solution that learns from offline simulations, shifting the intensive calculations required by iFE methods to an offline training stage and enabling online prediction of tissue deformation with reduced computational time. Our network was trained on data from numerous simulated needle insertions to capture interactions among insertion forces, tissue properties, and resulting motion. Once trained, the model produces predictions almost instantaneously, making it suitable for real-time applications. We validated the approach by steering the needle in a simulated deformable, moving gel to compare it with numerical-based methods, and then performing needle steering within a reconstructed human body that involves multiple structures and integrates the robot's dynamics. The results demonstrated that the developed networks achieved slightly better accuracy in the first scenario while also running faster, resulting in improved performance under the robot's dynamics. These findings show that our method is a promising advancement toward real-time guidance systems for needle-based medical procedures.