Automatic pre- to intra-operative CT registration for image-guided cochlear implant surgery

Abstract

Percutaneous cochlear implantation (PCI) is a minimally-invasive image-guided cochlear implant approach, where access to the cochlea is achieved by drilling a linear channel from the skull surface to the cochlea. The PCI approach requires pre- and intra-operative planning. Computation of a safe linear drilling trajectory is performed in a preoperative CT. This trajectory is mapped to intraoperative space using the transformation matrix that registers the pre- and intra-operative CTs. However, the difference in orientation between the pre- and intra-operative CTs is too extreme to be recovered by standard, gradient descent-based registration methods. Thus far, the registration has been initialized manually by an expert. In this paper, we present a method that aligns the scans completely automatically. We compared the performance of the automatic approach to the registration approach when an expert does the manual initialization on 11 pairs of scans. There is a maximum difference of 0.18 mm between the entry and target points of the trajectory mapped with expert initialization and the automatic registration method. This suggests that the automatic registration method is accurate enough to be used in a PCI surgery.

Fig. 1

Drill attached to the Microtable, which is mounted on the patient head.

Fig. 2

Intra-operative (blue and white) overlaid on pre-operative (black and white) CT image shown in axial (a), coronal (b) and sagittal (c) view.

Fig. 3

Registration flow chart.

Fig. 4

Images used in the level set initialization process. (a) Sagittal view of intra-operative CT, (b) H, the sheetness filter output, and (c) voxels used to estimate T_bone.

Fig. 5

Level set initialization process

Fig. 6

Shown in blue is the intensity histogram of voxels that have an intensity value greater than −100. Shown in red is the intensity histogram of voxels that have both an intensity value greater −100 and sheetness score greater than 0.5.

Fig. 7

Shown in green are the contours of the binary skull segmentation. Shown in yellow and red are the contours of c_m and of {C}−c_m.

Fig. 8

Result of level set segmentation. Shown in (a) in white are the contours of the cortical surface level set segmentation result. In (b) is a 3D triangle mesh representation of the resulting cortical surface.

Fig. 9

The distances α and β that are used for constructing the spin image at a vertex p.

Fig. 10

Reference intra-operative cortical surface. The color at each vertex encodes the curvature value.

Fig. 11

Cortical surface of reference and target intra-operative CT images. Three correspondences and their associated spin images are shown.