Reliable and accurate needle localization in curvilinear ultrasound images using signature-based analysis of ultrasound beamformed radio frequency signals

Abstract

Purpose: Ultrasound imaging is used in many minimally invasive needle insertion procedures to track the advancing needle, but localizing the needle in ultrasound images can be challenging, particularly at steep insertion angles. Previous methods have been introduced to localize the needle in ultrasound images, but the majority of these methods are based on ultrasound B-mode image analysis that is affected by the needle visibility. To address this limitation, we propose a two-phase, signature-based method to achieve reliable and accurate needle localization in curvilinear ultrasound images based on the beamformed radio frequency (RF) signals that are acquired using conventional ultrasound imaging systems.

Methods: In the first phase of our proposed method, the beamformed RF signals are divided into overlapping segments and these segments are processed to extract needle-specific features to identify the needle echoes. The features are analyzed using a support vector machine classifier to synthesize a quantitative image that highlights the needle. The quantitative image is processed using the Radon transform to achieve a reliable and accurate signature-based estimation of the needle axis. In the second phase, the accuracy of the needle axis estimation is improved by processing the RF samples located around the signature-based estimation of the needle axis using local phase analysis combined with the Radon transform. Moreover, a probabilistic approach is employed to identify the needle tip. The proposed method is used to localize needles with two different sizes inserted in ex vivo animal tissue specimens at various insertion angles.

Results: Our proposed method achieved reliable and accurate needle localization for an extended range of needle insertion angles with failure rates of 0% and mean angle, axis, and tip errors smaller than or equal to $0.7^{\circ }$ , 0.6 mm, and 0.7 mm, respectively. Moreover, our proposed method outperformed a recently introduced needle localization method that is based on B-mode image analysis.

Conclusions: These results suggest the potential of employing our signature-based method to achieve reliable and accurate needle localization during ultrasound-guided needle insertion procedures.

Keywords: needle localization; signature-based analysis; support vector machine classification; ultrasound image and signal processing; ultrasound imaging; ultrasound-guided needle insertion procedures.