Abstract
In this paper we describe a new algorithm for nonrigid registration of brain images based on an elastically deformable model. The use of registration methods has become an important tool for computer-assisted diagnosis and surgery. Our goal was to improve analysis in various applications of neurology and neurosurgery by improving nonrigid registration. A local gray level similarity measure is used to make an initial sparse displacement field estimate. The field is initially estimated at locations determined by local features, and then a linear elastic model is used to infer the volumetric deformation across the image. The associated partial differential equation is solved by a finite element approach. A model of empirically observed variability of the brain was created from a dataset of 154 young adults. Both homogeneous and inhomogeneous elasticity models were compared. The algorithm has been applied to medical applications including intraoperative images of neurosurgery showing brain shift and a study of gait and balance disorder.
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J. Ashburner and K.J. Friston: Spatial Normalization. In Brain Warping. Ed. Arthur W. Toga, (Academic Press) Ch.2:27–44, 1999.
S. Balay, W.D. Gropp, L.C. McInnes and B.F. Smith: PETSc 2.0 users manual. Tech. Rep. ANL-95/11-Revision 2.0.28, Argonne National Laboratory, 2000
R. Bajcsy and S. Kovacic: Multiresolution Elastic Matching. Computer Vison, Graphics and Image Processing, 46:1–21, 1989.
M. Bro-Nielsen: Finite element modeling in medical VR. Journal of the IEEE, 86(3):490–503, 1998.
D.L. Collins, G. Le Goualher and A.C. Evans: Non-linear Cerebral Registration with Sulcal Constrains. In MICCAI 1998, Cambridge, MA, USA, 1998 pages 974–984.
D.L. Collins: 3D Model-based segmentation of individual brain structures for magnetic resonance imaging data. PhD thesis, 1994.
C. Davatzikos: Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models. Comp. Vis. and Image Understanding, Special Issue on Medical Imaging, 66(2):207–222, May 1997
M. Ferrant, S.K. Warfield, A. Nabavi, F.A. Jolesz and R. Kikinis: Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model. In MICCAI 2000, Pittsburgh, Pennsylvania, USA, 2000, pages 19–28.
C.R.G. Guttmann, R. Benson, S.K. Warfield, X. Wei, M.C. Anderson, C. Hall, K. Abu-Hasaballah, J.P. Mugler and L. Wolfson: White matter abnormalities in mobility-impaired older persons. Neurology, 54:1277–1283, 2000
H. Lester, S.R. Arridge, K.M. Jansons, L. Lemieux, J.V. Hajnal and A. Oatridge: Non-linear Registration with the Variable Viscosity fluid Algorithm. In IPMI 1999, pages 238–251, 1999.
Y. Wang, L.H. Staib: Physical model-based non-rigid registration incorporating statistical shape information. Medical Image Analysis, 4(2000) pages 7–20, 2000.
S.K. Warfield, M. Ferrant, X. Gallez, A. Nabavi, F.A. Jolesz and R. Kikinis: Real-Time biomechanical Simulation of Volumetric Brain Deformation for Image Guided Neurosurgery. High Performance Networking and Computing Conference, Dallas, USA, 230:1–16, 2000.
S.K. Warfield, F.A. Jolesz and R. Kikinis: A High Performance Computing Approach to the Registration of Medical Imaging Data. Parallel Computing 24:1345–1368, 1998.
J. Weickert, B. ter Haar Romeny, M.A. Viergever: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Processing, Vol. 7, pages 398–410, March 1998.
J. Weickert: Anisotropic diffusion in image processing. Teubner Verlag, Stuttgart, 1997.
W.M. Wells, R. Kikinis, W.E.L. Grimson, F. Jolesz: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15:429–442, 1996
K.J. Worsley, S. Marrett, P. Neelin, A.C. Vandal, J.J. Friston and A.C. Evans: A unified Statistical Approach for Determining Significant Signals in Images of Cerebral Activation In Human Brain Mapping, pages 58–73, 1996
O.C. Zienkewickz, R.L. Taylor: The Finite Element Method. McGraw Hill Book Co., 1987.
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Rexilius, J. et al. (2001). A Novel Nonrigid Registration Algorithm and Applications. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_110
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DOI: https://doi.org/10.1007/3-540-45468-3_110
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