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Entropy 2017, 19(5), 187;

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

Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Lyon, France
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
Received: 10 March 2017 / Revised: 18 April 2017 / Accepted: 24 April 2017 / Published: 26 April 2017
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [1140 KB, uploaded 26 April 2017]   |  


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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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