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Open AccessArticle

Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke

1
Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Lyon, France
2
ENS-Lyon, UMR CNRS 5669 ‘UMPA’, and INRIA Alpes, project NUMED, F-69364 Lyon, France
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(5), 187; https://doi.org/10.3390/e19050187
Received: 10 March 2017 / Revised: 18 April 2017 / Accepted: 24 April 2017 / Published: 26 April 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
In longitudinal medical studies, multicomponent images of the tissues, acquired at a given stage of a disease, are used to provide information on the fate of the tissues. We propose a quantification of the predictive value of multicomponent images using information theory. To this end, we revisit the predictive information introduced for monodimensional time series and extend it to multicomponent images. The interest of this theoretical approach is illustrated on multicomponent magnetic resonance images acquired on stroke patients at acute and late stages, for which we propose an original and realistic model of noise together with a spatial encoding for the images. We address therefrom very practical questions such as the impact of noise on the predictability, the optimal choice of an observation scale and the predictability gain brought by the addition of imaging components. View Full-Text
Keywords: information theory; predictive power; stroke; tissue fate prediction information theory; predictive power; stroke; tissue fate prediction
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Giacalone, M.; Frindel, C.; Grenier, E.; Rousseau, D. Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke. Entropy 2017, 19, 187.

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