Medical Image Registration Incorporating Deformations

Multiple sources of 3D medical image data can be used to construct detailed patient representations. Typically registration is achieved assuming the validity of rigid body transformation. In many applications, and in particular when updating representations used for guidance during surgery and therapeutic interventions, this assumption is inappropriate. In this paper we describe a general method for 3D deformation, show how registration can incorporate a composite of rigid body and deformation components and illustrate this methodology on 3 example sets of images. The first is a repeated 3D MR scan of the abdomen of a volunteer who purposely changed position between scans; the second is an MR and CT scan of the head and neck, in which the patient was in a different position for the two scans; and the third is a set of MR and CT images of the head taken before and after epilepsy surgery. Non rigid deformation and composite warping showed significant improvement in registration accuracy in each case.

[1]  Charles R. Meyer,et al.  Simultaneous usage of homologous points, lines, and planes for optimal, 3-D, linear registration of multimodality imaging data , 1995, IEEE Trans. Medical Imaging.

[2]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Alan C. Evans,et al.  MRI-PET Correlation in Three Dimensions Using a Volume-of-Interest (VOI) Atlas , 1991, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  R. Mccoll,et al.  Spatial and temporal registration of cardiac SPECT and MR images: methods and evaluation. , 1991, Radiology.

[5]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[6]  Kanti V. Mardia,et al.  Image warping using derivative information , 1994, Optics & Photonics.

[7]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[8]  David N. Levin,et al.  Interactive 3-D Patient-Image Registration , 1991, IPMI.

[10]  Karl J. Friston,et al.  Plastic transformation of PET images. , 1991, Journal of computer assisted tomography.

[11]  K Herholz,et al.  Three-dimensional alignment of functional and morphological tomograms. , 1990, Journal of computer assisted tomography.

[12]  Fred L. Bookstein,et al.  Thin-Plate Splines and the Atlas Problem for Biomedical Images , 1991, IPMI.

[13]  D. Hill,et al.  Registration of MR and CT images for skull base surgery using point-like anatomical features. , 1991, The British journal of radiology.

[14]  Nira Dyn,et al.  Image Warping by Radial Basis Functions: Application to Facial Expressions , 1994, CVGIP Graph. Model. Image Process..

[15]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.