Feature-based multibody rigid registration of CT and ultrasound images of lumbar spine.

PURPOSE: Fusion of intraprocedure ultrasound and preprocedure CT data is proposed for guidance in percutaneous spinal injections, a common procedure for pain management. CT scan of the lumbar spine is usually collected in a supine position, whereas spinal injections are performed in prone or sitting positions. This leads to a difference in the spine curvature between CT and ultrasound images; as such, a single-body rigid registration approach cannot be used for the whole lumbar vertebrae.
METHODS: To compensate for the difference in the spinal curvature between the two imaging modalities, a multibody rigid registration algorithm is presented. The approach utilizes a point-based registration technique based on the unscented Kalman filter (UKF), taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming method, while the CT images are semiautomatically segmented using thresholding. The registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is developed to constrain the vertebrae transformation parameters during the registration and to ensure convergence.
RESULTS: The proposed methodology is evaluated on human spine phantoms and a sheep cadaver. Registrations on phantom data have a mean target registration error (TRE) of 1.99 mm in the region of interest and converged in 87% of cases. Registrations on sheep cadaver have a mean target registration error of 2.2 mm and converged in 82% of cases.
CONCLUSIONS: The proposed technique can robustly and simultaneously register several vertebrae extracted from CT images to the ultrasound volumes. The registration error below 2.2 mm is sufficient for most spinal injections.