A successful surface-based image-to-physical space registration in image-guidedliver surgery (IGLS) 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 (ICP) algorithm between the preoperative liversurface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liversurface 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 liversurface 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 liversurface. 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.
This work was supported under the National Institutes of Health (NIH) R21 Grant No. CA 91352-01 and by R21 Grant No. EB 007694-01 from the National Institute of Biomedical Imaging and Bioengineering of the NIH. The authors would like to thank Dr. Sean Glasgow, Mary Ann Laflin, and Krista Cstonos of Washington University School of Medicine in St. Louis, Missouri for their help in collecting the clinical range scan data used in this work. In addition, many of the algorithms and visualization tools used in this work were developed using the Visualization Toolkit (http://www.vtk.org). The FastRBF Toolkit (FarField Technology, Christchurch, NZ) was used to generate a number of the surfaces shown. The ANN nearest neighbor search library (http://www.cs.umd.edu/ mount/ANN/) was used to speed up closest point searches. Some segmentations of clinical data were performed using the ANALYZE AVW Version 6.0, which was provided in collaboration with the Mayo Foundation, Rochester, Minnesota. For disclosure, Drs. Chapman, Dawant, Galloway and Miga are founders and hold equity in Pathfinder Therapeutics, Inc., Nashville, TN.
II.A. Weighted patch ICP algorithm
II.A.1. Point correspondence determination
II.A.2. Weighted point-based registration
II.A.3. Dynamic weighting scheme
II.B. Phantom validation
II.B.1. Silicon liver phantom and phantom data acquisition
II.B.2. Phantom data robustness trials
II.C. Clinical validation
II.C.1. Clinical image and intraoperative data acquisition
II.C.2. Clinical data registration experiments
II.C.3. Clinical data robustness trials
III.A. Phantom data robustness trials
III.B. Clinical data registration experiments
III.C. Clinical data robustness trials
IV.A. Weighted patch ICP robustness and validation
IV.B. Algorithm parameter selection and optimization
IV.C. Segmentation effects on algorithm performance
IV.D. Concerns regarding intraoperative implementation
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