Algorithms for modeling anatomic and target volumes in image-guided neurosurgery and radiotherapy

Alakuijala, Jyrki (2001-11-19)
 
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Alakuijala, Jyrki
University of Oulu
19.11.2001
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Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:9514265742

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Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in Raahensali (Auditorium L 10), Linnanmaa, on December 7th, 2001, at 12 noon.
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Abstract

The use of image-guidance in surgery and radiotherapy has significantly improved patient outcome in neurosurgery and radiotherapy treatments. This work developed volume definition and verification techniques for image-guided applications, using a number of algorithms ranging from image processing to visualization.

Stereoscopic visualization, volumetric tumor model overlaid on an ultrasound image, and visualization of the treatment geometry were experimented with on a neurosurgical workstation. Visualization and volume definition tools were developed for radiotherapy treatment planning system.

The magnetic resonance inhomogeneity correction developed in this work, possibly the first published data-driven method with wide applicability, automatically mitigates the RF field inhomogeneity artefact present in magnetic resonance images. Correcting the RF inhomogeneity improves the accuracy of the generated volumetric models.

Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correctness. Moreover, the effects of different priority queue implementations were studied.

A fast algorithm for calculating high-quality digitally reconstructed radiographs has been developed and shown to better meet typical radiotherapy needs than the two alternative algorithms. A novel visualization method, beam’s light view, is presented. It uses texture mapping for projecting the fluence of a radiation field on an arbitrary surface.

This work suggests several improved algorithms for image processing, segmentation, and visualization used in image-guided treatment systems. The presented algorithms increase the accuracy of image-guidance, which can further improve the applicability and efficiency of image-guided treatments.

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