multicomponent and longitudinal imaging seen as a communication channel—an application to stroke

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ID: 217778
2017
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Abstract
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.
Reference Key
giacalone2017entropymulticomponent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Mathilde Giacalone;Carole Frindel;Emmanuel Grenier;David Rousseau
Journal European journal of medicinal chemistry
Year 2017
DOI
10.3390/e19050187
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