Percutaneous needle procedures are mostly carried out with the guidance of 2D ultrasound (US) imaging. US images are inherently noisy and their resolutions are low. Hence, target tracking can be challenging. Image based tracking methods can be used to track the needle and the target. This paper proposes visual tracking of multiple moving points, such as biopsy needles and targets, in 2D US images using normalized cross correlation and mutual information similarity functions. Both moving and deformable targets can be tracked. An affine motion model is used for small and moving target tracking and a thin plate spline motion model is used for deformable target tracking. During the tracking, needle and target template images are updated with a template update strategy. Also, tracking outputs of normalized cross correlation and mutual information are fused using the Kalman filter to reduce the tracking error. During the experiments, needle is inserted using a needle insertion robot. 2D US probe is attached to a robotic arm's end effector to servo the probe along the needle insertion path. Proposed needle and target tracking methods were tested with phantoms. Accuracies of the needle tip and moving target tracking methods were measured using an optical tracking system. Experimental results showed that the proposed tracking method could be used to simultaneously track the needle tip and the targets in real-time in 2D US guided percutaneous needle procedures.

Ultrasound (US) is one the most commonly used medical imaging techniques in percutaneous needle procedures. However, US images are inherently noisy and contain excessive number of artifacts. Hence, it is not easy to track the needle tip in the US images during the needle insertions. At this point, image based visual tracking techniques can be used for needle tip tracking. This paper presents a method for visual tracking of biopsy needles in 2D US images using sum of squared differences and sum of conditional variances. Second order Gauss-Newton optimization is used to decrease processing time and make the tracking more robust. The needle template images used in the method are updated with a strategy to prevent needle loss and detection failures during tracking. The paper also explains how to identify needle losses during tracking and how to recover the needle position without using a needle localization algorithm. We demonstrate the precision of the visual needle tip tracking method with experiments under challenging tracking conditions.