Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation

Registration of 3D point clouds using a local descriptor based on grid point normal

The coarse-to-fine method is the prime technology for point cloud registration in 3D reconstruction. Aimed at the problem of low accuracy of coarse registration for the partially overlapping point clouds, a novel, to the best of our knowledge, 3D local feature descriptor named grid normals deviation angles statistics (GNDAS) for aligning roughly pairwise point clouds is proposed in this paper. The descriptor is designed by first dividing evenly the local surface into some grids along the x axis and y axis of the local reference frame, then making the statistics of the deviation angles of normals at grid points. Based on the correspondences generated by matching descriptors and a transformation estimation method, the initial registration result is obtained. The coarse registration result is used as the initial value of the iterative closest point algorithm to achieve the refinement of the registration result. Experimental comparisons on two public datasets demonstrate that our GNDAS descriptor has high descriptiveness and strong robustness to noise at low level and varying mesh resolution. The registration results also confirm the superiority of our registration approach over previous versions in accuracy and efficiency.

Development and evaluation of a "trackerless" surgical planning and guidance system based on 3D Slicer

Conventional optical tracking systems use cameras sensitive to near-infrared (NIR) light and NIR illuminated/active-illuminating markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely used within the neurosurgical theater and is a staple in the standard of care for craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure in the OR with the patient in a fixed head orientation. We propose a framework to achieve this in the OR without conventional tracking technology, i.e., a "trackerless" approach. Briefly, we investigate an extension of the 3D Slicer which combines surgical planning and craniotomy designation. While taking advantage of the well-developed 3D Slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with an intraoperative magnetic resonance imaging-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without the use of a conventional optical tracking technology. To test this approach, four experienced neurosurgeons performed experiments on five different surgical cases using our 3D Slicer module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this craniotomy planning approach and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental realization of an integrated trackerless surgical guidance platform.