Rapid Path Planning Algorithm for Percutaneous Rigid Needle Biopsy Based on Optical Illumination Principles

Optimal needle trajectory selection is critical in biopsy procedures to minimize tissue damage and ensure diagnostic accuracy. Timely trajectory planning is essential, as it relies on preoperative CT imaging. Prolonged processing times increase the risk of patient movement, rendering the planned path invalid. Traditional methods relying on clinician expertise or slow algorithms struggle with complex anatomical modeling for structures such as blood vessels. We introduce a novel method that reframes trajectory planning as an optimal puncture site identification problem by leveraging optical principles and computer rendering. A 3D model of key anatomical structures is reconstructed from CT images and segmented using SegResNet (average Dice similarity coefficient of 0.9122). A virtual light source positioned at the target illuminates the space, assigning distinct absorption coefficients to tissues based on needle permissibility and risk. Diffuse reflection simulates needle angle, and accumulated absorption represents depth, capturing puncture constraints. This simulation generates a grayscale map on the skin surface, highlighting candidate puncture sites. Furthermore, we employ a random forest-based method to model clinician preferences. This model analyzes an RGB image derived from the grayscale distribution to automatically select the optimal path and determine the needle entry point. The experimental evaluation demonstrates an average computation time of just 1.905 s per sample, which is significantly faster than traditional methods that require seconds to minutes. Moreover, clinical assessment by a thoracic surgeon found that 78% of the recommended paths met clinical standards, with 0% deemed unsatisfactory. These findings suggest that our method provides a rapid, intuitive, and reliable decision-support tool, improving biopsy safety and efficiency.