A successful surface-based image-to-physical space registration in image-guided liver surgery ͑IGLS͒ is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point ͑ICP͒ algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.
In REF they propose a robust surface registration using salient anatomical features for the initial registration and a laser range scanner (LRS) is used to deform the model intraoperatively.
Robust surface registration using salient anatomical features for image-guided liver surgery: Algorithm and validation
Medical physics
Medical physics
Medicine