Algorithms for sparse X-ray CT image reconstruction of objects with known contour

dc.contributor.author Dogandžić, Aleksandar
dc.contributor.author Gu, Renliang
dc.contributor.author Gu, Renliang
dc.contributor.author Qiu, Kun
dc.contributor.department Center for Nondestructive Evaluation
dc.date 2018-02-13T06:05:03.000
dc.date.accessioned 2020-06-30T01:25:34Z
dc.date.available 2020-06-30T01:25:34Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2012
dc.date.embargo 2013-02-11
dc.date.issued 2011-07-01
dc.description.abstract <p>We develop algorithms for sparse X-ray computed tomography (CT) image reconstruction of objects with known contour, where the signal outside the contour is assumed to be zero. We first propose a constrained residual squared error minimization criterion that incorporates <em>both</em> the knowledge of the object's contour <em>and</em> signal sparsity in an appropriate transform domain. We then present convex relaxation and greedy approaches to approximately solving this minimization problem; our greedy mask iterative hard thresholding schemes guarantee monotonically non-increasing residual squared error. We also apply mask minimum norm (mask MN) and least squares (mask LS) methods that ignore signal sparsity and solve the residual squared error minimization problem that imposes only the object contour constraint. We compare the proposed schemes with existing large-scale sparse signal reconstruction methods via numerical simulations and demonstrate that, by exploiting both the object contour information in the underlying image and sparsity of its discrete wavelet transform (DWT) coefficients, we can reconstruct this image using a significantly smaller number of measurements than the existing methods. We apply the proposed methods to reconstruct images from simulated X-ray CT measurements and demonstrate their superior performance compared with the existing approaches.</p>
dc.description.comments <p>Copyright 2012 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.</p> <p>This article appeared in <em>AIP Conference Proceedings </em>1430 (2012): 597–604 and may be found at <a href="http://link.aip.org/link/doi/10.1063/1.4716282">http://dx.doi.org/10.1063/1.4716282</a>.</p>
dc.identifier archive/lib.dr.iastate.edu/cnde_conf/36/
dc.identifier.articleid 1025
dc.identifier.contextkey 3684184
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cnde_conf/36
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/15654
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/cnde_conf/36/2011_Dogandzic_AlgorithmsSparseXray.pdf|||Fri Jan 14 23:46:07 UTC 2022
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.disciplines Materials Science and Engineering
dc.subject.keywords computerised tomography
dc.subject.keywords discrete wavelet transforms
dc.subject.keywords image reconstruction
dc.subject.keywords iterative methods
dc.subject.keywords least squares approximations
dc.subject.keywords minimisation
dc.subject.keywords X-ray imaging
dc.subject.keywords nondestructive evaluation
dc.subject.keywords QNDE
dc.subject.keywords Electrical and Computer Engineering
dc.title Algorithms for sparse X-ray CT image reconstruction of objects with known contour
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication cc1b7de1-984e-484b-90a6-9957532f7d13
relation.isOrgUnitOfPublication f2b877c3-5654-4c6a-9e64-6c944f9f02b6
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