Deformable registration of multimodal data including rigid structures

Multimodality imaging studies are becoming more widely utilized in the analysis of medical data. Anatomical data from computed tomography (CT) and magnetic resonance imaging (MRI) are useful for, analyzing or further processing functional data from techniques such as positron emission tomography and single photon emission computed tomography (SPECT). When data are not acquired simultaneously, even when these data are acquired on a dual-imaging device using the,same bed, motion can occur that requires registration between the reconstructed image volumes. As the human torso can allow nonrigid motion, this type of motion should be estimated and corrected. The authors report a deformation registration technique that utilizes rigid registration for bony structures while allowing elastic transformation of soft tissue to more accurately register the entire image volume. The technique is applied to the registration of CT and MR images of the lumbar spine. First, a global rigid registration.. is performed to approximately align features. Bony structures aide then segmented from the CT data, using a sentiautomated process, and bounding boxes for each vertebra are established. Each CT subvolume is then individually registered to the MRI data using a piece-wise rigid registration algorithm and a mutual information image similarity measure. The resulting set of rigid transformations allows for accurate registration of the parts of the CT and MRI data representing the vertebrae but not the adjacent soft tissue. To align the soft tissue, a smoothly varying deformation is computed using a thin plate spline (TPS) algorithm. The T S technique requires a sparse set of landmarks that are to be brought into correspondence. These landmarks are automatically obtained from the segmented data using simple edge-detection techniques and random sampling from the edge candidates. A smoothness parameter is also included in the TPS formulation for characterization of the stiffness of the soft tissue. Estimation of an appropriate stiffness factor is obtained iteratively by using the mutual information cost function on the result of the global deformable transformation.