Salient anatomical features for robust surface registration and atlas-based model updating in image-guided liver surgery

Abstract

Image-guided surgery (IGS) has emerged as a valuable tool for the interactive incorporation of pre-operative image data into the surgical setting. While a majority of research has focused on neurosurgical procedures, the feasibility of implementing IGS methods in hepatic procedures has become evident. However, a lack of robustness in the methods developed for performing image-guided liver surgery (IGLS) has impaired their utility. In order to improve the robustness of the algorithms used within IGLS, the incorporation of salient anatomical features that can be reliably identified on the hepatic anatomy is proposed within this work. More specifically, these anatomical features can be weighted within the performance of the image-to-physical space mapping, or registration, that is required for display of the intra-operative location of surgical instruments within the context of pre-operative image data. Additionally, the salient anatomical feature registration can be used to quantify the extent of soft tissue deformation that is known to compromise the intra-operative registration due to the use of rigid body assumptions in determining the mathematical mapping. The quantification and analysis of soft tissue deformation within the context of IGLS provides unique insight into the design of algorithms that can be used to compensate for the shift induced guidance errors. Based on the deformation studies, a novel atlas-based model updating method is proposed for the improvement of IGLS guidance accuracy. The atlas-based method relies on the utilization of pre-operatively computed model solutions to expedite the determination of a non-rigid image-to-physical space mapping. Further, the atlas-based method incorporates the hepatic salient anatomical features to improve the robustness of the method.