Needle detection using ultrasound B-mode and power Doppler analyses

Background Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertion angles. Purpose A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppler analyses. Methods A small buzzer is used to excite the needle and an ultrasound system is utilized to acquire B-mode and power Doppler images for the needle. The B-mode and power Doppler images are processed using Radon transform and local-phase analysis to initially detect the axis of the needle. The detection of the needle axis is improved by processing the power Doppler image using alpha shape analysis to define a region of interest (ROI) that contains the needle. Also, a set of feature maps is extracted from the ROI in the B-mode image. The feature maps are processed using a machine learning classifier to construct a likelihood image that visualizes the posterior needle likelihoods of the pixels. Radon transform is applied to the likelihood image to achieve an improved needle axis detection. Additionally, the region in the B-mode image surrounding the needle axis is analyzed to identify the needle tip using a custom-made probabilistic approach. Our method was utilized to detect needles inserted in ex vivo animal tissues at shallow [20 circle-40 circle$20<^>\circ -40<^>\circ$), moderate [40 circle-60 circle$40<^>\circ -60<^>\circ$), and steep [60 circle-85 circle$60<^>\circ -85<^>\circ$] angles. Results Our method detected the needles with failure rates equal to 0% and mean angle, axis, and tip errors less than or equal to 0.7 degrees, 0.6 mm, and 0.7 mm, respectively. Additionally, our method achieved favorable results compared to two recently introduced needle detection methods. Conclusions The results indicate the potential of applying our method to achieve effective needle detection in ultrasound images.