Abstract
We present multivariate statistics to detect intensity changes in longitudinal, multimodal, three-dimensional MRI data from patients with multiple sclerosis (MS). Working on a voxel-by-voxel basis, and considering that there is at most one such change-point in the time series of MR images, two complementary statistics are given, which aim at detecting disease activity. We show how to derive these statistics in a Neyman-Pearson framework, by computing ratios of data likelihood under null and alternative hypotheses. Preliminary results show that it is possible to detect both lesion activity and brain atrophy in this framework.
Chapter PDF
Similar content being viewed by others
Keywords
- Multiple Sclerosis
- Brain Atrophy
- American Statistical Association
- Multivariate Statistic
- Nuisance Parameter
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Prima, S., Ayache, N., Janke, A., Francis, S.J., Arnold, D.L., Collins, D.L.: Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 363–371. Springer, Heidelberg (2002)
Chernoff, H., Zacks, S.: Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time. Annals of Mathematical Statistics 35(3), 999–1018 (1964)
Gardner, L.A.: On Detecting Changes in the Mean of Normal Variates. Annals of Mathematical Statistics 40(1), 116–126 (1969)
Sled, J.G., Zijdenbos, A.P.: A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space. J. of Computer Assisted Tomography 18(2), 192–205 (1994)
Berger, J.O., Liseo, B., Wolpert, R.L.: Integrated likelihood methods for eliminating nuisance parameters. Statistical Science 14(1), 1–28 (1999)
Srivastava, M.S., Worsley, K.J.: Likelihood Ratio Tests for a Change in the Multivariate Normal Mean. Journal of The American Statistical Association 81(393), 199–204 (1979)
Hawkins, D.M.: Testing a Sequence of Observations for a Shift in Location. Journal of The American Statistical Association 72(357), 180–186 (1977)
Worsley, K.J.: On the Likelihood Ratio Test for a Shift in Location of Normal Populations. Journal of The American Statistical Association 74(366), 365–367 (1979)
Sen, A.K., Srivastava, M.S.: On Multivariate Tests for Detecting Change in Mean. Sankhya: The Indian Journal of Statistics 35(2), 173–186 (1973)
Sen, A.K., Srivastava, M.S.: On Tests for Detecting Change in Mean When Variance Is Unknown. Annals of the Institute of Statistical Mathematics 27, 479–486 (1975)
Sen, A.K., Srivastava, M.S.: Some One-Sided Tests for Change in Level. Technometrics 17, 61–64 (1975)
von Neumann, J., Kent, R.H., Bellinson, H.R., Hart, B.I.: The mean square successive difference. Annals of Mathematical Statistics 12, 153–162 (1941)
Sen, A.K., Srivastava, M.S.: On Tests for Detecting Change in Mean. Annals of Statistics 3(1), 98–108 (1975)
Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J., Evans, A.C.: A unified statistical approach or determining significant signals in images of cerebral activation. Human Brain Mapping 4, 58–73 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prima, S., Arnold, D.L., Collins, D.L. (2003). Multivariate Statistics for Detection of MS Activity in Serial Multimodal MR Images. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39899-8_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-39899-8_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20462-6
Online ISBN: 978-3-540-39899-8
eBook Packages: Springer Book Archive