Deep Learning-Based 3D Freehand Ultrasound Reconstruction with Inertial Measurement Units

Raphael Prevost, Mehrdad Salehi, Simon Jagoda, Navneet Kumar, Julian Sprung, Alexander Ladikos, Robert Bauer, Oliver Zettinig, Wolfgang Wein

Abstract:

This work aims at reconstructing 3D ultrasound volumes from sequences of freehand images acquired with standard 2D probes without any expensive or cumbersome external tracking hardware.
We extend our previous method based on deep learning to integrate and also learn from measurements of an inertial measurement unit (IMU).
Our system is evaluated on a dataset of 600 in vivo ultrasound sweeps, yielding accurate reconstructions with a median normalized drift of 5.2% even on long sweeps with complex trajectories, hence paving the way towards translation into clinical routine.

Keywords: 3D freehand ultrasound, deep learning, motion estimation, inertial measurement unit

Author Affiliation: ImFusion GmbH, piur Imaging GmbH