Abstract
In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). This approach has particularly interesting properties since it estimates large, smooth and invertible optimal deformations having a rich descriptive power for the quantification of temporal changes in the images. First, we show the importance of the smoothing kernel and its influence on the final solution. We then propose a new strategy for the spatial regularization of the deformations, which uses simultaneously fine and coarse smoothing kernels. We have evaluated the approach on both 2D synthetic images as well as on 3D MR longitudinal images out of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Results highlight the regularizing properties of our approach for the registration of complex shapes. More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications.
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Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)
Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113 (2007)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 12, 26–41 (2008)
Beg, F.M., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)
Crum, W., Tanner, C., Hawkes, D.: Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging. Physics in Medicine and Biology 50(21), 5153–5174 (2005)
Dupuis, P., Grenander, U., Miller, M.I.: Variational problems on flows of diffeomorphisms for image matching. Q. Appl. Math. LVI(3), 587–600 (1998)
Haber, E., Modersitzki, J.: Cofir: coarse and fine image registration. In: SIAM Real-Time PDE-Constrained Optimization, pp. 37–49 (2007)
Hernandez, M., Bossa, M.N.,Olmos, S.: Registration of anatomical images using paths of diffeomorphisms parameterized with stationary vector field flows. Int. J. Comput. Vision 85(3), 291–306 (2009)
Lorenzen, P., Prastawa, M., Davis, B., Gerig, G., Bullitt, E., Joshi, S.: Multi-modal image set registration and atlas formation. Med. Image Anal. 10(3), 440–451 (2006)
Miller, M., Younes, L.: Group actions, homeomorphisms, and matching: A general framework. International Journal of Computer Vision 41(1-2), 61–84 (2001)
Risser, L., Vialard, F., Murgasova, M., Holm, D., Rueckert, D.: Large diffeomorphic registration using fine and coarse strategies. application to the brain growth characterization. In: International Workshop on Biomedical Image Registration -WBIR, pp. 186–197 (2010)
Schuff, N., Woerner, N., Boreta, L., Kornfield, T., Shaw, L.M., Trojanowski, J.Q., Thompson, P.M., Jack, J.C.R., Weiner, M.W.: Disease Neuroimaging Initiative: MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 132(4), 1067–1077 (2009)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)
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Risser, L., Vialard, FX., Wolz, R., Holm, D.D., Rueckert, D. (2010). Simultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer’s Disease. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_75
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DOI: https://doi.org/10.1007/978-3-642-15745-5_75
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