In many types of percutaneous needle insertion surgeries, tissue deformation and needle deflection can create significant difficulties for accurate needle placement. In this paper, we present a method for automatic needle tracking in 2D ultrasound (US) images, which is used in a needle–tissue interaction model to estimate current and future needle tip deflection. This is demonstrated using a semi-automatic needle steering system. The US probe can be controlled to follow the needle tip or it can be stopped at an appropriate position to avoid tissue deformation of the target area. US images are used to fully parameterize the needle-tissue model. Once the needle deflection reaches a pre-determined threshold, the robot rotates the needle to correct the tip’s trajectory. Experimental results show that the final needle tip deflection can be estimated with average accuracies between 0.7[Formula: see text]mm and 1.0[Formula: see text]mm for insertions with and without rotation. The proposed method provides surgeons with improved US feedback of the needle tip deflection and minimizes the motion of the US probe to reduce tissue deformation of the target area.

Abstract-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.

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Abstract-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.