Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data

TL;DR

This paper introduces a neural network combined with Kalman filtering to accurately and robustly track 3D positions of objects in ultrasound imaging using 2D linear array data, achieving real-time performance.

Contribution

It presents a novel method integrating neural network-based out-of-plane offset estimation with Kalman filtering for improved 3D ultrasound object tracking.

Findings

Mean error of 0.1mm in simulations

Mean error of 0.2mm in experimental data

Effective 3D localization for elevational distances >1mm

Abstract

Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 6mm considered for a 25mm aperture.