In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.

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 workflow of the surgical team. Laser-range scanned (LRS) partial surface measurements can be taken intraoperatively with relatively little impact on the surgical workflow, 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 tissuemechanics-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. PURPOSELiver resection surgery in the open abdomen is a challenging setting for the application of image-guided surgical techniques which have been largely limited to procedures involving the cranium in the past. Surgical liver presentation typically begins with mobilization from the surrounding anatomy, followed by stabilization by packing support material underneath and around the organ. Thus large deformations (on the order of several centimeters) often occur between the preoperative (when CT imaging was performed) and intraoperative organ states.While intraoperative imaging has been used to document the extent of deformation, 1 and guidance solutions using intraoperative imaging have been proposed, 1-5 the workflow requirements and the challenges of integrating preoperative imaging data continue to be a hindrance. As a result, there remains a clinical need to efficiently align preoperative data to the intraoperative patient state, in order to leverage the wealth of preoperative image data that can be collected without incurring the encumbrance of many imaging systems. In prior work, Clements, et al. proposed a robust weighted-patch iterative-closest-point algorithm to perform rigid registration using a surface point cloud obtained from a laser range scan (LRS) combined with salient feature data patches from tooltip swabbing. 6 Subsequently, Dumpuri et al. 7 and Clements et al. 8 investigated methods for an additional nonrigid registration step. Using a linear elastic finite element model generated from the patient's CT data, these methods imposed Dirichlet b...

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