Le prochain séminaire de l'équipe A3SI du LIGM (unité mixte de recherche de l'Université Paris Est) aura lieu le jeudi 10 novembre de 13h30 à 14h30 ESIEE Paris, salle 260.
Abstract: During the past two decades, much of the research in image analysis was devoted to image registration, producing a large number of free software solutions. Most applications, mainly in the biomedical domain, require deformable image registration, in which a nonlinear dense transformation is sought (as opposed to a linear or global one) due to the fact that almost all anatomical parts, or organs of the human body are deformable structures.
In this talk, a joint deformable registration and diffusion modeling approach will be presented, which aims to improve estimation of the apparent diffusion coefficient (ADC) in diffusion-weighted (DW) magnetic resonance imaging (MRI). Over the last years, ADC computed from DW-MRI has become an important imaging biomarker for evaluating and managing patients with neoplastic or cerebrovascular disease. Standard methods for the calculation of ADC ignore the presence of noise and motion between successive (in time) DW-MR images acquired by changing the (operator-selected) b-value parameter. In order to accurately quantify the diffusion process during image acquisition, a registration method is introduced that is based on a high-order Markov Random Fields (MRF) formulation. In this formulation, inference is expressed as a (undirected) graph optimization problem acting on a predefined graph structure associated with a discrete number of variables.