Multirigid registration of MR and CT images of the cervical spine

Abstract

We present our work on fusion of MR and CT images of the cervical spine. To achieve the required registration accuracy of approximately 1mm, the spine is treated as a collection of rigid vertebrae, and a separate rigid body transformation applied to each (Hawkes). This in turn requires segmentation of the CT datasets into separate vertebral images, which is difficult because the narrow planes separating adjacent vertebrae are parallel to the axial plane of the CT scans. We solve this problem by evolving all the vertebral contours simultaneously using a level set method, and use contour competition to estimate the position of the vertebral edges when a clean separation between adjacent vertebrae is not seen. Contour competition is based in turn on the vertical scan principle: no part of a given vertebra is vertically below any part of an inferior vertebra. Once segmentation is complete, the individual rigid body transforms are then estimated using mutual information maximization, and the CT images of the vertebrae superimposed on the MR scans. The resultant fused images contain the bony detail of CT and the soft tissue discrimination of MR and appear to be diagnostically equivalent, or superior, to CT myelograms. A formal test of these conclusions is planned for the next phase of our work.