Registration of CT and MR brain images using a combination of points and surfaces

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Abstract
In this paper we present a hybrid registration technique that uses a weighted combination of several feature shapes, e.g. points and surfaces. The technique is a modification of Besl & McKay's iterative closest point (ICP) algorithm (IEEE Trans. Pattern Anal. Mach. Intell., 14:239-256, 1992). We use the technique to register x-ray computed tomography (CT) and T2-weighted magnetic resonance (MR) volume head images acquired from eleven patients that underwent craniotomies in a neurosurgical clinical trial. Each patient had five external markers attached to transcutaneous posts screwed into the outer table of the skull. We define registration error as the distance between corresponding marker positions after registration and transformation. The CT and MR images are registered using points (marker positions) only, surfaces only, and a weighted combination of points and surfaces. The CT surfaces are derived from contours corresponding to the inner surface of the skull. The MR surfaces are derived from contours corresponding to the cerebrospinal fluid (CSF)-dura interface. Registration using points and surfaces is more accurate than registration using only points or surfaces.

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