日 時 平成29年11月30日(木)14:30~16:10
会 場 熊本大学黒髪南地区 総合研究棟2F 204号室
講 師 Université Côte d'Azur, CNRS, LEAT, France   Professor Christian Pichot
演 題 Numerical Modeling and High-Speed Parallel Computing - New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring
講演概要 A stroke, or cerebrovascular accident (CVA), is classically characterized as a neurological deficit attributed to an acute focal injury of the central nervous system (CNS) by a vascular cause, including cerebral infarction, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH), and is a major cause of disability and death worldwide [1]. About 85% of strokes are ischemic due to cerebral infarction, caused by an interruption of the blood supply to some part of the brain, 15% are hemorrhagic (10% primary and 5% subarachnoid hemorrhage) [2]. Differentiating between these different types of strokes is an essential part of the initial workup of the patients because the subsequent management and treatment of each patient is vastly different. Rapid and accurate diagnosis is crucial.
This talk deals with microwave tomography for brain stroke imaging using state-of-the-art numerical modeling and massively parallel computing. Iterative microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient based) with successive solutions of a direct problem such as the accurate modeling of a whole-microwave measurement system. Moreover, a sufficiently high number of unknowns is required to accurately represent the solution. As the system will be used for detecting the brain stroke (ischemic or hemorrhagic) as well as for monitoring during the treatment, running times for the reconstructions should be reasonable. The method used is based on high-order finite elements, parallel preconditioners from the Domain Decomposition method and Domain Specific Language with open source FreeFEM solver.
参加費 無料
問合せ 熊本大学 福迫武
開 催 主催:IEEE AP-S Fukuoka Chapter


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