Neural-Guided RRT*: Learning-Based Planning of Entry Point and Puncture Path for Steerable Bevel-Tip Needle Insertion

Flexible needle percutaneous puncture demands precise and efficient path planning to ensure surgical safety and success. However, traditional algorithms often struggle to balance real-time performance and planning quality in high-resolution, complex three-dimensional environments. This paper introduces a Neural-Guided RRT^* (NG-RRT^*) algorithm to address these challenges. First, we propose a 3D U-Net based entry points selection method, leveraging spatial features to generate the probability distributions of optimal entry points. Second, we develop a neural-guided non-uniform sampling strategy which utilizes an optimal path prediction network to improve the sampling efficiency of RRT^*. Additionally, we introduce a native arc trajectory search strategy that directly generates kinematically feasible curve segments, reducing path complexity and improving smoothness. Simulation results demonstrate that the proposed NG-RRT^* outperforms previous methods in path quality, computational efficiency, and adaptability to complex scenarios, and exhibits good clinical feasibility in actual anatomical environments. This work provides a robust and efficient solution for flexible needle path planning, advancing the field of medical navigation systems.