An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling

Camille Maumet 1 Pierre Maurel 1 Jean-Christophe Ferré 2 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we introduce a new locally multivariate procedure to quantitatively extract voxel-wise patterns of abnormal perfusion in individual patients. This a contrario approach uses a multivariate metric from the computer vision community that is suitable to detect abnormalities even in the presence of closeby hypo- and hyper-perfusions. This method takes into account local information without applying Gaussian smoothing to the data. Furthermore, to improve on the standard a contrario approach, which assumes white noise, we introduce an updated a contrario approach that takes into account the spatial coherency of the noise in the probability estimation. Validation is undertaken on a dataset of 25 patients diagnosed with brain tumors and 61 healthy volunteers. We show how the a contrario approach outperforms the massively univariate General Linear Model usually employed for this type of analysis.
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Submitted on : Thursday, April 7, 2016 - 4:08:53 PM
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Camille Maumet, Pierre Maurel, Jean-Christophe Ferré, Christian Barillot. An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling. NeuroImage, Elsevier, 2016, 134, ⟨10.1016/j.neuroimage.2016.03.054⟩. ⟨inserm-01291748v2⟩

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