The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery

Michael I. Miga; Prashanth Dumpuri; Amber L. Simpson; Jared A Weis; William R Jarnagin

doi:10.1117/12.878696

1 March 2011 The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery

Michael I. Miga, Prashanth Dumpuri, Amber L. Simpson, Jared A Weis, William R Jarnagin

Author Affiliations +

Proceedings Volume 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling; 79640C (2011) https://doi.org/10.1117/12.878696
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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).

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Michael I. Miga, Prashanth Dumpuri, Amber L. Simpson, Jared A Weis, and William R Jarnagin "The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79640C (1 March 2011); https://doi.org/10.1117/12.878696

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