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Proceedings Paper

The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery
Author(s): Michael I. Miga; Prashanth Dumpuri; Amber L. Simpson; Jared A Weis; William R Jarnagin
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Paper Abstract

The problem of extrapolating cost-effective relevant information from distinctly finite or sparse data, while balancing the competing goals between workflow and engineering design, and between application and accuracy is the 'sparse data extrapolation problem'. Within the context of open abdominal image-guided liver surgery, one realization of this problem is compensating for non-rigid organ deformations while maintaining workflow for the surgeon. More specifically, rigid organ-based surface registration between CT-rendered liver surfaces and laser-range scanned intraoperative partial surface counterparts resulted in an average closest-point residual 6.1 ± 4.5 mm with maximumsigned distances ranging from -13.4 to 16.2 mm. Similar to the neurosurgical environment, there is a need to correct for soft tissue deformation to translate image-guided interventions to the abdomen (e.g. liver, kidney, pancreas, etc.). While intraoperative tomographic imaging is available, these approaches are less than optimal solutions to the sparse data extrapolation problem. In this paper, we compare and contrast three sparse data extrapolation methods to that of datarich interpolation for the correction of deformation within a liver phantom containing 43 subsurface targets. The findings indicate that the subtleties in the initial alignment pose following rigid registration can affect correction up to 5- 10%. The best deformation compensation achieved was approximately 54.5% (target registration error of 2.0 ± 1.6 mm) while the data-rich interpolative method was 77.8% (target registration error of 0.6 ± 0.5 mm).

Paper Details

Date Published: 1 March 2011
PDF: 8 pages
Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79640C (1 March 2011); doi: 10.1117/12.878696
Show Author Affiliations
Michael I. Miga, Vanderbilt Univ. (United States)
Prashanth Dumpuri, Pathfinder Therapeutics Inc. (United States)
Amber L. Simpson, Vanderbilt Univ. (United States)
Jared A Weis, Vanderbilt Univ. (United States)
William R Jarnagin, Memorial Sloan-Kettering Cancer Ctr. (United States)

Published in SPIE Proceedings Vol. 7964:
Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; David R. Holmes III, Editor(s)

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