Abstract
Bias in image registration has to be accounted for when performing morphometric studies. The presence of bias can lead to unrealistic power estimates and can have an adverse effect in group separation studies. Most image registration algorithms are formulated in an asymmetric fashion and the solution is biased towards the transformation direction. The popular free-form deformation algorithm has been shown to be a robust and accurate method for medical image registration. However, it suffers from the lack of symmetry which could potentially bias the result. This work presents a symmetric and inverse-consistent variant of the free form deformation.
We first assess the proposed framework in the context of segmentation-propagation. We also applied it to longitudinal images to assess regional volume change. In both evaluations, the symmetric algorithm outperformed a non-symmetric formulation of the free-form deformation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Boyes, R., Rueckert, D., Aljabar, P., Whitwell, J., Schott, J., Hill, D., Fox, N.: Cerebral atrophy measurements using Jacobian integration: Comparison with the boundary shift integral. Neuroimage 32(1), 159–169 (2006)
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(1), 26–41 (2008)
Tao, G., He, R., Datta, S., Narayana, P.A.: Symmetric inverse consistent nonlinear registration driven by mutual information. Comput. Meth. Prog. Bio. 95(2), 105–115 (2009)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)
Thompson, W.K., Holland, D., Initiative, A.D.N.: Bias in tensor based morphometry stat-ROI measures result in unrealistic power estimates. NeuroImage 57(1), 1–4 (2011); discussion 5–14
Hua, X., Gutman, B., Boyle, C.P., Rajagopalan, P., Leow, A.D., Yanovsky, I., Kumar, A.R., Toga, A.W., Jack, C.R., Schuff, N., Alexander, G.E., Chen, K., Reiman, E.M., Weiner, M.W., Thompson, P.M.: Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry. NeuroImage 57(1), 5–14 (2011)
Fox, N.C., Ridgway, G.R., Schott, J.M.: Algorithms, atrophy and Alzheimer’s disease: cautionary tales for clinical trials. NeuroImage 57(1), 15–18 (2011)
Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., Christensen, G., Collins, D., Gee, J., Hellier, P., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration 46(3), 786–802 (July 2009)
Rohlfing, T., Maurer Jr., C.R., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraints. IEEE Transactions on Medical Imaging 22(6), 730–741 (2003)
Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic Registration Using B-Splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part II. LNCS, vol. 4191, pp. 702–709. Springer, Heidelberg (2006)
Sdika, M.: A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization. IEEE Transactions on Medical Imaging 27(2), 271–281 (2008)
Feng, W., Reeves, S., Denney, T., Lloyd, S., Dell’Italia, L., Gupta, H.: A new consistent image registration formulation with a b-spline deformation model. In: Rosen, B., Brooks, D. (eds.) IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 979–982 (2009)
Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Transactions on Medical Imaging 20(7), 568–582 (2001)
Ashburner, J., Friston, K.J.: Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 7(4) (June 1999)
Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K., Eubank, W.: PET-CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging 22(1), 120–128 (2003)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Meth. Prog. Bio. 98(3), 278–284 (2010)
Modat, M., Ridgway, G.R., Daga, P., Cardoso, M.J., Ashburner, J., Ourselin, S.: Parametric non-rigid registration using a stationary velocity field. In: Zhou, S.K., Duncan, J.S., Ourselin, S. (eds.) IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA (2012)
Shattuck, D., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K., Poldrack, R., Bilder, R., Toga, A.: Construction of a 3D probabilistic atlas of human cortical structures 39(3), 1064–1080 (February 2008)
Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image and Vision Computing 19(1-2), 25–31 (2001)
Yushkevich, P.A., Avants, B.B., Das, S.R., Pluta, J., Altinay, M., Craige, C., Initiative, A.D.N.: Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3 T MRI data. NeuroImage 50(2), 434–445 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Modat, M., Cardoso, M.J., Daga, P., Cash, D., Fox, N.C., Ourselin, S. (2012). Inverse-Consistent Symmetric Free Form Deformation. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-31340-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31339-4
Online ISBN: 978-3-642-31340-0
eBook Packages: Computer ScienceComputer Science (R0)