Improving Puncture Accuracy Based on the Estimation and Compensation of the Rotation of the Flexible Needle Tip

Needle puncture serves as the foundation for a broad range of minimally invasive surgical diagnostic and therapeutic procedures. Flexible needles have the potential to overcome the limitations of traditional rigid needles, particularly their inability to bypass obstacles and correct needle tip deviations, making them a promising alternative. However, when the needle hub rotates during insertion, torsional interactions between the needle shaft and surrounding tissue result in a discrepancy between the rotation angles of the needle hub and the needle tip. This discrepancy can lead to needle tip misalignment, thereby affecting patient prognosis and compromising diagnostic reliability. Furthermore, accurately measuring the orientation of the needle tip remains a challenge. To address these issues, we propose a method for estimating and compensating for orientation discrepancies, which improves puncture accuracy, enhances patient prognosis, and boosts diagnostic reliability. Specifically, based on puncture images, we introduce a projection-based approach to estimate the actual rotation angle of the needle tip, and construct a dataset containing the corresponding rotation angles of the needle hub and tip. We then utilize a neural network to estimate the true orientation of the needle tip from a specific needle hub rotation angle. Experimental results demonstrate that the proposed method can effectively estimate and compensate for the discrepancy between the orientations of the needle tip and hub, reducing the average rotation error from 9.6° to 0.6°. Additionally, puncture experiments conducted using paths planned via the particle swarm optimization algorithm verify that the method improves puncture precision, decreasing the error from 2.42±0.65 mm to 0.96±0.65 mm. These results indicate that the method is feasible and effective for enhancing patient prognosis and diagnostic reliability.