Nonrigid liver registration for image-guided surgery using partial surface data: a novel iterative approach

D. Caleb Rucker; Yifei Wu; Janet E. Ondrake; Thomas S. Pheiffer; Amber L. Simpson; Michael I. Miga

doi:10.1117/12.2007991

8 March 2013 Nonrigid liver registration for image-guided surgery using partial surface data: a novel iterative approach

D. Caleb Rucker, Yifei Wu, Janet E. Ondrake, Thomas S. Pheiffer, Amber L. Simpson, Michael I. Miga

Author Affiliations +

Proceedings Volume 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling; 86710B (2013) https://doi.org/10.1117/12.2007991
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In the context of open abdominal image-guided liver surgery, the efficacy of an image-guidance system relies on its ability to (1) accurately depict tool locations with respect to the anatomy, and (2) maintain the work flow of the surgical team. Laser-range scanned (LRS) partial surface measurements can be taken intraoperatively with relatively little impact on the surgical work flow, as opposed to other intraoperative imaging modalities. Previous research has demonstrated that this kind of partial surface data may be (1) used to drive a rigid registration of the preoperative CT image volume to intraoperative patient space, and (2) extrapolated and combined with a tissue-mechanics-based organ model to drive a non-rigid registration, thus compensating for organ deformations. In this paper we present a novel approach for intraoperative nonrigid liver registration which iteratively reconstructs a displacement field on the posterior side of the organ in order to minimize the error between the deformed model and the intraopreative surface data. Experimental results with a phantom liver undergoing large deformations demonstrate that this method achieves target registration errors (TRE) with a mean of 4.0 mm in the prediction of a set of 58 locations inside the phantom, which represents a 50% improvement over rigid registration alone, and a 44% improvement over the prior non-iterative single-solve method of extrapolating boundary conditions via a surface Laplacian.

Citation Download Citation

D. Caleb Rucker, Yifei Wu, Janet E. Ondrake, Thomas S. Pheiffer, Amber L. Simpson, and Michael I. Miga "Nonrigid liver registration for image-guided surgery using partial surface data: a novel iterative approach", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710B (8 March 2013); https://doi.org/10.1117/12.2007991

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