Enhancement of instrumented ultrasonic tracking images using deep learning

Abstract

Purpose: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images. Methods: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network. Results: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm. Conclusion: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor

“…For these reasons, further work is required to improve the out-of-plane tracking provided by the UNT system. It is likely that the relative amplitudes of the signals received by the FOH from each aperture of the US probe encode its elevational position, as has been shown in similar work [ 54 ], and some preliminary work has started looking at using machine learning techniques to decode this, as has been demonstrated in the literature [ 55 , 56 , 57 ]. In [ 54 ], a photoacoustic beacon, rather than FOH, is embedded in the surgical instrument, and transmissions from it are received by the imaging probe and used to determine its 3D position.…”