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dc.creatorBroschat, Shira L.
dc.creatorZhang, Xiaodong
dc.creatorFlynn, Patrick J.
dc.date.accessioned2016-05-23T16:36:27Z
dc.date.available2016-05-23T16:36:27Z
dc.date.issued2002-06
dc.identifier.urihttp://hdl.handle.net/2376/6113
dc.description.abstractIn this paper, a new technique for solving the two-dimensional inverse scattering problem for ultrasound inverse imaging is presented. Reconstruction of a two-dimensional object is accomplished using an iterative algorithm which combines the conjugate gradient (CG) method and a neural network (NN) approach. The neural network technique is used to exploit knowledge of the statistical characteristics of the object to enhance the performance of the conjugate gradient method. The results for simulations show that the CGNN algorithm is more accurate than the CG method and, in addition, convergence occurs more rapidly. For the CGNN algorithm, approximately 50% fewer iterations are needed to obtain the inverse solution for a signal-to-noise ratio (SNR) of 50 dB. For a smaller SNR of 35 dB, the CGNN method is not as accurate, but it still gives reasonable results. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218396X02001644?journalCode=jcaen_US
dc.languageEnglish
dc.publisherWorld Scientific Publishing
dc.rightsIn copyright
dc.rightsopenAccess
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.rights.urihttp://purl.org/eprint/accessRights/OpenAccess
dc.subjectConjugate gradient method
dc.subjectUltrasound imaging
dc.subjectInverse problems
dc.titleA conjugate gradient-neural network technique for ultrasound inverse imaging
dc.typeArticle
dc.description.versionPost-print
dc.description.citationZhang, X., S. L. Broschat, P. J. Flynn, A conjugate gradient-neural network technique for ultrasound inverse imaging, J. Computational Acoustics, Vol. 10, No. 2, 243-264, Jun. 2002.


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  • Broschat, Shira
    This collection features research and educational materials by Shira Broschat, Professor and Curriculum Coordinator for the School of Electrical Engineering and Computer Science at Washington State University.

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