Insights Into Three-Dimensional Ultrasound Mosaicing

The creation of 2D ultrasound mosaics is becoming a
common clinical practice with a high clinical value.
The next step coming along with the increasing
availability of 2D array transducers is the creation
of 3D mosaics. In the literature of ultrasound
registration, the alignment of multiple images has
not yet been addressed. Therefore, we propose
registration strategies, which are able to cope with
problems arising by multiple image alignment. We use
pair-wise registration with a consecutive Lie
normalization and simultaneous registration, which
urges the usage of multivariate similarity measures.
In this thesis, we propose alternative multivariate
extensions based on a maximum likelihood framework.
Due to the higher computational cost of simultaneous
registration, we describe possibilities for speeding
them up, among others, the usage of a stochastic
pyramid.

We also present methods to reduce speckle noise and
detect shadow in US volumes. For the compounding of
the final volume, a variety of approaches are
listed. Experimental results show the good
performance of the proposed registration strategies
and similarity measures.

Autorentext
Studied Computer Science and Mathematics at
TU München. He was additionally enrolled at CDTM for studying
tchnology management. The author spent one academic year at a
grande école in Paris, and subsequently six months in
Princeton. From 2007 on, he is doing his PhD in the inter-
disciplinary field of medical imaging.

Klappentext
The creation of 2D ultrasound mosaics is becoming a common clinical practice with a high clinical value. The next step coming along with the increasing availability of 2D array transducers is the creation of 3D mosaics. In the literature of ultrasound registration, the alignment of multiple images has not yet been addressed. Therefore, we propose registration strategies, which are able to cope with problems arising by multiple image alignment. We use pair-wise registration with a consecutive Lie normalization and simultaneous registration, which urges the usage of multivariate similarity measures. In this thesis, we propose alternative multivariate extensions based on a maximum likelihood framework. Due to the higher computational cost of simultaneous registration, we describe possibilities for speeding them up, among others, the usage of a stochastic pyramid. We also present methods to reduce speckle noise and detect shadow in US volumes. For the compounding of the final volume, a variety of approaches are listed. Experimental results show the good performance of the proposed registration strategies and similarity measures.