Sensorless Out-of-Plane Displacement Estimation for Freehand 3D Ultrasound Applications

Abstract

The aim of freehand 3D ultrasound imaging is to construct a 3D volume of data from conventional 2D ultrasound images. In freehand 3D ultrasound, the probe is moved by hand over the area of interest in an arbitrary manner and its motion is measured by attaching some kind of position sensor to the probe. Since attaching external tracking sensor to the probe imposes some difficulties, alternative ways are investigated to acquire freehand 3D ultrasound without a position sensor. Sensorless Estimation of any in-plane motion between images is reliably determined using standard image registration techniques and the big challenge is out-of-plane motion estimation. The most important approach to overcome this challenge so far is to use the speckle decorrelation method. The method is based on the idea that the correlation value of a specific model of speckle known as Fully Developed Speckle (FDS) can be used to estimate the out-of-plane displacement between images. However, the method requires the B-scans to contain mostly regions of FDS pattern but this kind of pattern is rare in scans of real tissue. One successful way around this problem is to quantify the amount of coherency at each point in the B-scans by calculating the axial and lateral correlations and comparing them with the FDS calibrated ones. This approach leads to adapt elevational decorrelation curves based on the amount of non-FDS regions in the image. The novelty of this thesis is firstly adjusting the method to be applicable on B-mode ultrasound images rather than RF ultrasound data because RF data is not always available in clinical environments. Secondly, the experiment setup is truly freehand and the motion of the probe is not constrained in any directions during scanning and in-plane motion compensation is required. Thirdly, the method is tested on in vivo human data as well as test chicken and beef data sets. The method is shown to work quite remarkable (accuracy of around 5%) for the elevational distance estimation for both test phantoms and real human tissue data.ii Acknowledgements I would like to use this opportunity to thank my supervisor Dr. Chris Joslin, Associate Professor of School of Information Technology, Carleton University, for his friendly guidance, inspiration and assistance.