Abstract
We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
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References
Audette, M.: Anatomical Surface Identifcation, Range-sensing and Registration for Characterizing Intrasurgical Brain Deformations. PhD thesis, McGill University (2003)
Ferrant, M., Nabavi, A., Macq, B., Black, P., Jolesz, F., Kikinis, R., Warfield, S.: Serial registration of intraoperative MR images of the brain. Medical Image Analysis 6, 337–360 (2002)
Yeung, F., Levinson, S., Fu, D., Parker, K.: Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh. IEEE Transactions on Medical Imaging 17, 945–956 (1998)
Rohr, K., Stiehl, H., Sprengel, R., Buzug, T., Weese, J., Kuhn, M.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on Medical Imaging 20, 526–534 (2001)
Rexilius, J., Warfield, S.K., Guttmann, C.R.G., Wei, X., Benson, R., Wolfson, L., Shenton, M.E., Handels, H., Kikinis, R.: A novel nonrigid registration algorithm and applications. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 923–931. Springer, Heidelberg (2001)
Frey, P.J., George, P.L.: Mesh Generation. Hermes Science Publications (2000)
Rousseeuw, P.: Least median-of-squares regression. Journal of the American Statistical Association 79, 871–880 (1984)
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© 2005 Springer-Verlag Berlin Heidelberg
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Clatz, O. et al. (2005). Hybrid Formulation of the Model-Based Non-rigid Registration Problem to Improve Accuracy and Robustness. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_37
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DOI: https://doi.org/10.1007/11566489_37
Publisher Name: Springer, Berlin, Heidelberg
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