Abstract
The automatic analysis of longitudinal changes between Diffusion Tensor Imaging (DTI) acquisitions is a promising tool for monitoring disease evolution. However, few works address this issue and existing methods are generally limited to the detection of changes between scalar images characterizing diffusion properties, such as Fractional Anisotropy or Mean Diffusivity, while richer information can be exploited from the whole set of Apparent Diffusion Coefficient (ADC) images that can be derived from a DTI acquisition. In this paper, we present a general framework for detecting changes between two sets of ADC images and we investigate the performance of four statistical tests. Results are presented on both simulated and real data in the context of the follow-up of multiple sclerosis lesion evolution.
We would like to thank the ARSEP (Association pour la Recherche sur la Sclérose En Plaques) and the Région Alsace for their support.
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Keywords
- Multiple Sclerosis
- Fractional Anisotropy
- Longitudinal Change
- Statistical Detection
- Fractional Anisotropy Image
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Boisgontier, H., Noblet, V., Renard, F., Heitz, F., Rumbach, L., Armspach, JP. (2009). Statistical Detection of Longitudinal Changes between Apparent Diffusion Coefficient Images: Application to Multiple Sclerosis. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_118
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DOI: https://doi.org/10.1007/978-3-642-04268-3_118
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