Target error for image-to-physical space registration: Preliminary clinical results using laser range scanning

In this paper, preliminary results from an image-to-physical space registration platform are presented. The current platform employs traditional and novel methods of registration which use a variety of data sources to include: traditional synthetic skin-fiducial point-based registration, surface registration based on facial contours, brain feature point-based registration, brain vessel-to-vessel registration, and a more comprehensive cortical surface registration method that utilizes both geometric and intensity information from both the image volume and physical patient. The intraoperative face and cortical surfaces were digitized using a laser range scanner (LRS) capable of producing highly resolved textured point clouds. In two in vivo cases, a series of registrations were performed using these techniques and compared within the context of a true target error. One of the advantages of using a textured point cloud data stream is that true targets among the physical cortical surface and the preoperative image volume can be identified and used to assess image-to-physical registration methods. The results suggest that iterative closest point (ICP) method for intraoperative face surface registration is equivalent to point-based registration (PBR) method of skin fiducial markers. With regard to the initial image and physical space registration, for patient 1, mean target registration error (TRE) were 3.1 +/- 0.4 mm and 3.6 +/- 0.9 mm for face ICP and skin fiducial PBR, respectively. For patient 2, the mean TRE were 5.7 +/- 1.3 mm, and 6.6 +/- 0.9 mm for face ICP and skin fiducial PBR, respectively. With regard to intraoperative cortical surface registration, SurfaceMI outperformed feature based PBR and vessel ICP with 1.7 +/- 1.8 mm for patient 1. For patient 2, the best result was achieved by using vessel ICP with 1.9 +/- 0.5 mm.