T. Reichl Online Error Correction for the Tracking of Laparoscopic Ultrasound Diploma Thesis. Technische Universität München, July 2007 (bib)
	In abdominal surgery, laparoscopic ultrasound is widely used for minimally invasive procedures. Because of the missing visual feedback, it is often difficult for the surgeons to relate the flexible ultrasound transducer to patient anatomy and images. Utilizing instrument tracking techniques for navigation and augmented visualization can therefore provide great benefits for minimally invasive procedures. Electromagnetic systems are the only currently available means to determine the pose of the transducer tip inside the patient, its position and orientation. However, the electromagnetic field can be distorted in various ways, leading to erroneous measurements. Different error correction techniques have been developed, but their application to laparoscopic ultrasound is either difficult or they require an additional calibration procedure before each intervention. Additionally, no techniques have yet been proposed for the compensation of dynamic sources of error. In this thesis two new methods for online error detection and correction for the tracking of flexible laparoscopic ultrasound probes are presented. The first method utilizes magneto-optic tracking of the ultrasound transducer shaft combined with electromagnetic tracking of the transducer tip. Deviations between optical and electromagnetic tracking of the transducer shaft are used to estimate the distortion of the electromagnetic field at the transducer tip. The second and more sophisticated method involves a mathematical model of the movements of the flexible transducer tip. All necessary parameters are computed offline in a distortion-free environment and remain valid until the sensors are repositioned. During an intervention the model is fitted to the measurements of the electromagnetic sensor at the transducer tip. Both methods were rigorously tested in experiments and comprehensively evaluated in comparison to related work. Our results are very promising and especially the model based approach improves the current state of art for both error detection and correction.
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