Cortical surface registration for image-guided neurosurgery using laser-range scanning

Abstract

In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 +/- 0.2, 2.0 +/- 0.3, and 1.2 +/- 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 +/- 1.0, 0.9 +/- 0.6, and 1.3 +/- 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

Fig. 1

Example of brain shift seen using an intraoperative image-guided surgery system. The crosshairs indicate the location of the surgical probe in image space, in this case inside the brain. In reality, the probe is touching the surface of the brain near the superior temporal gyrus.

Fig. 2

The 3-D Digital RealScan USB and its use in the operating room. (a) Close up of the scanner showing the laser emit window in the middle and the CCD and laser received cameras on the right. (b) LRS in the operating room covered with sterile isolation bag and mounted on custom built vibration damping monopod (shown here in collapsed state). (c) LRS in the OR, covered in sterile bag and mounted to overhead swing arm.

Fig. 3

Three views of the surface extracted from a patient-specific gadolinium enhanced MR volume.

Fig. 4

The watermelon phantom used in this paper for registration accuracy experiments. (a) Watermelon with Omnipaque soaked twine laid into carved vessel grooves. (b) Acustar imaging marker filled with CT/MR contrast enhancement fluid. (c) Acustar divot caps for localization using Optotrak.

Fig. 5

Localized points in img, opto, and lrs. (a) Volume rendering of image data showing markers (letters) and manually localized landmarks (numbers) in opto and img. (b) Landmarks localized in lrs space.

Fig. 6

Simulated deep tissue sampling. The larger sphere demonstrates the geometric sphere fit of the point cloud. The smaller sphere represents a sampling region with radius of 50 mm, centered about the centroid of the localized fiducials. The volume of overlap demonstrates the deep tissue sampling region.

Fig. 7

Intraoperative FOV. (a) Digital photograph with the surgeon highlighting the vein of Trolard, a significant vessel in the area of therapy. (b) Textured point cloud generated intraoperatively using our LRS.

Fig. 8

TRE histogram for deep tissue targets using PBR-based registration on surface landmarks, ICP-based registration on surface contours, and SurfaceMI on textured surfaces.

Fig. 9

Three-dimensional distribution of TRE for deep tissue targets. The left column shows a top-down view of the watermelon surface with the TRE distribution shown for PBR (top), ICP (middle), and SurfaceMI (bottom). The right column shows the respective front views of the TRE distribution. Each deep tissue sample of TRE is grayscale encoded on the hemispheric surface shown. The range of scalar values is shown in the color bar associated with each figure.

Fig. 10

Results of ICP and SurfaceMI on intermodality registration of two textured surfaces. ICP registration conditions are shown in the top row with perturbed initial condition shown left and ICP registered shown right. SurfaceMI registration conditions are shown in the bottom row with perturbed initial condition shown left and SurfaceMI registered shown right. It should be noted that there is a texture projected on the surface of the watermelon that is an artifact of the rendering process, i.e., this texture did not affect the registration process. A gross-scale representation of the texture, which is a result of the slice-to-slice spacing in the CT image, can be seen in Fig. 5(a) for comparison.

Fig. 11

Fiducial registration error distribution given initial landmark perturbation. The landmarks in the FOV were perturbed up to ±2.5° in each spherical coordinate (ϕ, ψ, θ) in img.

Fig. 12

Registration results from intraoperative data. (a) The result of PBR-based registration using manually localized landmarks in img and lrs. (b) ICP registration using highlighted contours in img and lrs. (c) SurfaceMI registration given the initial alignment provided by the PBR method. The highlighted contours are prominent sulcal and vessel patterns visible in both spaces.