• Title/Summary/Keyword: University of Oxford

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Bio-inspired leaf stent for direct treatment of cerebral aneurysms: design and finite element analysis

  • Zhou, Xiang;You, Zhong;Byrne, James M.D.
    • Smart Structures and Systems
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    • v.8 no.1
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    • pp.1-15
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    • 2011

  • Cerebral aneurysm is common lesion among adult population. Current methods for treating the disease have several limitations. Inspired by fern leaves, we have developed a new stent, called leaf stent, which can provide a tailored coverage at the neck of an aneurysm and thus prevent the blood from entering the aneurysm. It alone can be used to treat the cerebral aneurysm and therefore overcomes problems existing in current treating methods. The paper focuses on the numerical simulation of the leaf stents. The mechanical behaviour of the stent in various designs has been investigated using the finite element method. It has been found that certain designs provide adequate radial force and have excellent longitudinal flexibility. The performance of certain leaf stents is comparable and even superior to those of the commercially available cerebral stents such as the Neuroform stent and the Enterprise stent, commonly used for stent assisted coiling, while at the same time, providing sufficient coverage to isolate the aneurysm without using coils.

  • https://doi.org/10.12989/sss.2011.8.1.001 인용 KSCI

Regularized Multichannel Blind Deconvolution Using Alternating Minimization

  • James, Soniya;Maik, Vivek;Karibassappa, K.;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.4 no.6
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    • pp.413-421
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    • 2015

  • Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) / L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera / mobile cameras.

  • https://doi.org/10.5573/IEIESPC.2015.4.6.413 인용 PDF KSCI