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Kim, Yunho
Mathematical Imaging Lab
Research Interests
  • Optimization, inverse problems, convex analysis, calculus of variations, partial differential equations, computational mathematics, medical/biomedical imaging

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HARDI DATA DENOISING USING VECTORIAL TOTAL VARIATION AND LOGARITHMIC BARRIER

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dc.contributor.author Kim, Yunho ko
dc.contributor.author Thompson, Paul M. ko
dc.contributor.author Vese, Luminita A. ko
dc.date.available 2014-11-17T08:11:43Z -
dc.date.created 2014-11-14 ko
dc.date.issued 2010-05 ko
dc.identifier.citation INVERSE PROBLEMS AND IMAGING, v.4, no.2, pp.273 - 310 ko
dc.identifier.issn 1930-8337 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/8923 -
dc.description.abstract In this work, we wish to denoise HARDI (High Angular Resolution Diffusion Imaging) data arising in medical brain imaging. Diffusion imaging is a relatively new and powerful method to measure the three-dimensional profile of water diffusion at each point in the brain. These images can be used to reconstruct fiber directions and pathways in the living brain, providing detailed maps of fiber integrity and connectivity. HARDI data is a powerful new extension of diffusion imaging, which goes beyond the diffusion tensor imaging (DTI) model: mathematically, intensity data is given at every voxel and at any direction on the sphere. Unfortunately, HARDI data is usually highly contaminated with noise, depending on the b-value which is a tuning parameter pre-selected to collect the data. Larger b-values help to collect more accurate information in terms of measuring diffusivity, but more noise is generated by many factors as well. So large b-values are preferred, if we can satisfactorily reduce the noise without losing the data structure. Here we propose two variational methods to denoise HARDI data. The first one directly denoises the collected data S, while the second one denoises the so-called sADC (spherical Apparent Diffusion Coefficient), a field of radial functions derived from the data. These two quantities are related by an equation of the form S = S(0) exp (-b.sADC) (in the noise-free case). By applying these two different models, we will be able to determine which quantity will most accurately preserve data structure after denoising. The theoretical analysis of the proposed models is presented, together with experimental results and comparisons for denoising synthetic and real HARDI data. ko
dc.description.statementofresponsibility open -
dc.language 영어 ko
dc.publisher AMER INST MATHEMATICAL SCIENCES ko
dc.title HARDI DATA DENOISING USING VECTORIAL TOTAL VARIATION AND LOGARITHMIC BARRIER ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-77954586505 ko
dc.identifier.wosid 000279070900005 ko
dc.type.rims ART ko
dc.description.wostc 2 *
dc.description.scopustc 2 *
dc.date.tcdate 2015-05-06 *
dc.date.scptcdate 2014-11-14 *
dc.identifier.doi 10.3934/ipi.2010.4.273 ko
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=77954586505 ko
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