Paper
30 May 2003 Intraoperative registration of the liver for image-guided surgery using laser range scanning and deformable models
Author Affiliations +
Abstract
The development of image-guided surgical systems (IGS) has had a significant impact on clinical neurosurgery and the desire to extend these principles to other surgical endeavors is the next step in IGS evolution. An impediment to its widespread adoption is the realization that the organ of interest often deforms due to common surgical loading conditions. As a result, alignment degradation between patient and the MR/CT image volume can occur which can compromise guidance fidelity. Recently, computational approaches to correct alignment have been proposed within neurosurgery. In this work, these approaches are extended for use within image-guided liver surgery and demonstrate this framework's adaptability. Results from the registration of the preoperative segmented liver surface and the intraoperative liver, as acquired by a laser range scanner, demonstrate accurate visual alignment in regions that deform minimally while in other regions misalignment due to deformations on the order of 1 cm are apparent. A model-updating strategy is employed which uses the closest point operator to compensate for deformations within the patient-specific image volume. The framework presented is an approach whereby laser range scanning coupled to a computational model of soft tissue deformation provide the necessary information to extend IGS principles to intra-abdominal explorative surgery applications.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael I. Miga, David Marshall Cash, Zhujiang Cao, Robert L. Galloway Jr., Benoit M. Dawant, and William C. Chapman "Intraoperative registration of the liver for image-guided surgery using laser range scanning and deformable models", Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, (30 May 2003); https://doi.org/10.1117/12.480216
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CITATIONS
Cited by 16 scholarly publications and 7 patents.
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KEYWORDS
Liver

Surgery

Tumors

Image-guided intervention

Lawrencium

Data modeling

Image registration

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