Deep learning for sensorless 3D freehand ultrasound imaging

Abstract

3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7% over the whole length of each ultrasound clip.