Abstract
This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These testing tools have been considered either for the comparison of tensor eigenvalues or eigenvectors, thus enabling to differentiate orientation and diffusivity changes. Results on simulated MS lesion evolutions and on real data are presented. Interestingly, experiments on an MS patient highlight the ability of the proposed approach to detect changes in non evolving lesions (according to conventional MRI) and around lesions (in the normal appearing white matter), which might open promising perspectives for the follow-up of the MS pathology.
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Keywords
- Multiple Sclerosis
- Receiver Operating Characteristic Curve
- Multiple Sclerosis Patient
- Change Detection
- Generalize Likelihood Ratio Test
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Grigis, A., Noblet, V., Renard, F., Heitz, F., Armspach, JP., Rumbach, L. (2010). Change Detection in Diffusion MRI Using Multivariate Statistical Testing on Tensors. 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_15
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DOI: https://doi.org/10.1007/978-3-642-15745-5_15
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