3D Needle Shape Estimation in TRUS-Guided Prostate Brachytherapy Using 2D Ultrasound Images

In this paper we propose an automated method to reconstruct the 3D needle shape during needle insertion procedures using only 2D transverse ultrasound (US) images. Using a set of transverse US images, image processing and random sample consensus (RANSAC) is 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 3D needle shape for verification. Results showed that the use of at least 3 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 (PPB) is also presented, where the estimated needle shape is compared to manual segmentation and sagittal US images. Our method is intended to help assess needle placement during manual or robotassisted needle insertion procedures after the needle has been inserted.