A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data

Abstract

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.

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

“…Phantom data were acquired consistent with a previously reported study by Rucker et al [13]. Briefly, a compliant hepatic phantom was created to mimic clinical organ deformation based on our experience in a previously reported 75 patient multi-center clinical trial [23].…”

“…With respect to modeling nonrigid behaviors, we employ a linear elastic biomechanical model of the preoperative organ described previously [13]. Large deformations and more sophisticated constitutive models are possible; however, applying a rigid registration followed by smaller nonrigid deformations is a first order deformation correction approach which, when considering localization errors and tracking accuracy, is appropriate.…”

“…In our work, we have concentrated on approaches that minimize differences between surfaces extracted from the preoperative imaging and those gathered intraoperatively. Our approaches use patient-specific biomechanical models to nonrigidly align the data [1], [6], and [13] (adding very sparse subsurface data, i.e. tumor centroid location in [13], has also been pursued).…”

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

“…Such interventions can include resection or ablative procedures depending upon patient and tumor indications [12][13][14]. Many techniques for guiding these surgical interventions have been explored, including mapping pre-procedure imaging to the surgical field or the integration of novel intraoperative image guidance systems [15][16][17]. Prior work has demonstrated the potential of Raman spectroscopy as an intraoperative tool for guiding resection of breast tumors [7] but applying a similar technique in the liver would be challenging due to the strong background autofluorescence that overwhelms the Raman scattered signal [18,19].…”

Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery

“…After an initial rigid registration of the laser scan and CT surfaces, the residual closest point distances between the rigidly registered surfaces are minimized using a computational approach. The method was further refined by Rucker et al 4 using a tissue mechanics model subjected to boundary conditions, which were adjusted for liver resection therapy.…”