Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
This provides accurate real-time positional information for interventional surgical procedures using ultrasound, though it is incremental as it builds on existing Kalman filtering and neural network methods.
The paper tackled the problem of 3D object tracking from 2D linear array ultrasound data by combining a neural network for out-of-plane offset estimation with Kalman filtering to improve robustness and reduce noise, achieving mean errors of 0.1mm in simulations and 0.2mm in experiments for axial and lateral coordinates.
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.