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Abstract-Ultrasound-guided medical interventions are broadly applied in diagnostics and therapy, e.g. regional anesthesia or ablation. A guided intervention using 2D ultrasound is challenging due to the poor instrument visibility, limited field of view and the multi-fold coordination of the medical instrument and ultrasound plane. Recent 3D ultrasound transducers can improve the quality of the image-guided intervention if an automated detection of the needle is used. In this paper, we present a novel method for detecting medical instruments in 3D ultrasound data that is solely based on image processing techniques and validated on various ex-vivo and in-vivo datasets. In the proposed procedure, the physician is placing the 3D transducer at the desired position and the image processing will automatically detect the best instrument view, so that the physician can entirely focus on the intervention. Our method is based on classification of instrument voxels using volumetric structure directions and robust approximation of the primary tool axis. A novel normalization method is proposed for the shape and intensity consistency of instruments to improve the detection. Moreover, a novel 3D Gabor wavelet transformation is introduced and optimally designed for revealing the instrument voxels in the volume, while remaining generic to several medical instruments and transducer types. Experiments on diverse datasets including in-vivo data from patients show that for a given transducer and instrument type, high detection accuracies are achieved with position errors smaller than the instrument diameter in the 0.5 to 1.5 millimeter range on average.

Elastography, which uses ultrasound to image the tissue strain that results from an applied displacement, can display tumours and heat-ablated tissue with high contrast. However, its application to liver in vivo may be problematic due to the presence of respiratory and cardiovascular sources of displacement. The aim of this study was to measure the cardiovascular-induced component of natural liver motion for the purpose of planning future work that will either use the motion to produce elasticity images or will compensate for it when employing an external source of displacement. A total of 36 sequences of 7 s real-time radio frequency (RF) echo images of the liver were acquired from six healthy volunteers during breath-hold using a stationary 3.5 MHz transducer. For each image sequence, the axial and lateral components of displacement were measured for each pair of consecutive RF images using 2D-echo tracking. The spatio-temporal character of these displacements was then analysed using a novel approach, employing proper orthogonal decomposition, whereby the dominant motion patterns are described by eigenvectors with the highest eigenvalues. The motion patterns of different liver segments were complex, but they were also found to be cyclic, highly repeatable and capable of producing measurable displacements in the liver. These observations provide good evidence to suggest that it may be possible to correct for natural liver motion when using an externally applied displacement for elasticity imaging. It was also found that about 65%-70% of all liver motion could be described using the first eigenvector. Use of only this component of the motion will greatly simplify the design of a mechanical system to be used in an objective study of elasticity imaging of phantoms and excised tissues in the presence of simulated cardiovascular-induced liver motion.