Abstract
New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automatized algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data. The paper extends the usual concept of similarity in intensity (scalar) data to vector and tensor cases. A discussion on appropriate template selection and on the limitations of the template matching approach to incorporate the vector and tensor reorientation is also offered. Our approach to registration is based on a multiresolution scheme based on local matching of areas with a high degree of local structure and subsequent interpolation. Consequently we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator that outperforms conventional polynomial methods for the interpolation of sparse vector fields. The feasibility of the approach is illustrated by results on synthetic and clinical data.
This work has been partially funded by the Spanish Gov. (MEC), visiting research fellowship FPU PRI1999-0175 for the first author, jointly by the European Commission and the Spanish Gov. (CICYT), research grant 1FD97-0881-C02-01, by grant RG 3094A1/T from the National Multiple Sclerosis Society (SKW) and by US grants NIH NCRR P41 RR13218, NIH P01 CA67165 and NIH R01 RR11747. A. Nabavi has been supported by the DFG (NA 365/ 1-1)
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References
Roche, A., et al.: Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 555–566. Springer, Heidelberg (1999)
Poggio, T., Torre, V., Koch, C.: Computational vision and regularization theory. Nature 317, 314–319 (1985)
Moon, T.K., Stirling, W.W.: Matehematical Methods and Algorithms for Signal Processing. Prentice-Hall, NJ (2000)
Starck, J.L., Mrtagh, F., Bijaoui, A.: Image Processing and Data Analysis. The Multiscale Approach. Cambridge University Press, UK (1998)
Segel, L.A.: Mathematics Applied to Continuum Mechanics. Dover, NY (1987)
Rohr, K.: On 3D Differential Operators for Detecting Point Landmarks. Image and Vision Computing 15, 219–233 (1997)
Krige, D.: A Statistical Approach to Some Mine Valuation and Allied Problems on the Witwatersrand, Master Thesis, University of Witwatersrand (1951)
Cressie, N.: Kriging Nonstationary Data. Journal of the American Statistical Association 81, 625–634 (1986)
Parrott, R.W., et al.: Towards Statiscally Optimal Interpolation for 3D Medical Imaging. IEEE Engineering in Medicine and Biology, 49–59 (September 1993)
Peled, S., et al.: Magnetic Resonance Imaging Shows Orientation and Asymmetry of White Matter Tracts. Brain Research 780, 27–33 (1998)
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Ruiz-Alzola, J., Westin, C.F., Warfield, S.K., Nabavi, A., Kikinis, R. (2000). Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_55
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DOI: https://doi.org/10.1007/978-3-540-40899-4_55
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