A temporal 3D-registration framework for computer-integrated surgery

Ronald Backman

Research output: ThesisDoctoral Thesis

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Traditionally, volumetric modalities such as CT and MRI have provided static snapshots of anatomy enabling insight into the progression of disease and to the severity of injury. Recently, 3D-registration algorithms, originating in the neurosurgical field, have been used to merge these images resulting in richer visualizations. However, in situations where trauma patients are unable to be moved or are at risk of infection, there have been comparatively few advances. This thesis presents a 3D-registration framework that supports longitudinal study of morphologic changes in surface images of the upper body based on an optical technique - structured light imaging. The framework incorporates soft-tissue deformation modeling to allow coordinate frame determination and specific point tracking required for applications of Computer-Integrated Surgery. The framework is implemented in three stages using a coarse-fine approach that separately addresses the different sources of registration error commonly found in temporal registration applications. The coarse stage defines seven thoracic fiducials that form a rigid body. A special anthropomorphic stand is designed and used to enforce a rigid body assumption. Experimental results show the fiducials to have precision of approximately 2 mm. The medium stage incorporates the novel use of ultraviolet light as a surface registration technique. UV is used to avoid error caused when the projected light stripes interfere with the marker material - a common problem with external landmarks and optical assessment systems. A semi-automatic algorithm for identifying the centre of the fiducials is given and shown to be highly accurate - to within 1 pixel precision compared to the visually assessed centre. The movement of these fiducials is also modelled at the extremes of the respiratory cycle with individual fiducials moving from 5-17 mm. A least-squares algorithm is implemented to bring surfaces together based on their fiducial locations and rigid-body motion. This algorithm results in RMS error of approximately 1.17 +/- 0.45 mm. The fine stage involves finding fixed point correspondences in changed regions between a base surface and a comparison surface acquired at a different time given the rigid body registration from the previous stages. Five algorithmic variants are assessed using two simulations of thoracic swelling. The results do not show statistical significance between variants but do indicate visually some promising results. An application of this framework could be the near real-time guidance of the FAROArm, a precision measuring instrument commonly used in Computer-Integrated Surgery, to these points. This would facilitate the collection of functional information of clinical interest while maintaining positional congruence with data acquired at a different time point.
Original languageEnglish
QualificationDoctor of Philosophy
Publication statusUnpublished - 1999


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