In this paper, we propose an automated method to reconstruct the three-dimensional (3-D) needle shape during needle insertion procedures using only 2-D transverse ultrasound (US) images. Using a set of transverse US images, image processing and random sample consensus are used to locate the needle within each image and estimate the needle shape. The method is validated with an in vitro needle insertion setup and a transparent tissue phantom, where two orthogonal cameras are used to capture the true 3-D needle shape for verification. Results showed that the use of at least three images obtained at 75% of the maximum insertion depth or greater allows for maximum needle shape estimation errors of less than 2 mm. In addition, the needle shape can be calculated consistently as long as the needle can be identified in 30% of the transverse US images obtained. Application to permanent prostate brachytherapy is also presented, where the estimated needle shape is compared to manual segmentation and sagittal US images. Our method is intended to help to assess needle placement during manual or robot-assisted needle insertion procedures after the needle has been inserted.

This paper introduces an automatic method to visualize 3D needle shapes for reliable assessment of needle placement during needle insertion procedures. Based on partial observations of the needle within a small sample of 2D transverse ultrasound images, the 3D shape of the entire needle is reconstructed. An intensity thresholding technique is used to identify points representing possible needle locations within each 2D ultrasound image. Then, a Random Sample and Consensus (RANSAC) algorithm is used to filter out false positives and fit the remaining points to a polynomial model. To test this method, a set of 21 transverse ultrasound images of a brachytherapy needle embedded within a transparent tissue phantom are obtained and used to reconstruct the needle shape. Results are validated using camera images which capture the true needle shape. For this experimental data, obtaining at least three images from an insertion depth of 50 mm or greater allows the entire needle shape to be calculated with an average error of 0.5 mm with respect to the measured needle curve obtained from the camera image. Future work and application to robotics is also discussed.