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Main articleRights Statements InC/1.0 , Open Access
Journal article
A conjugate gradient-neural network technique for ultrasound inverse imaging
Journal of computational acoustics., Vol.10(2), pp.243-264
06/2002
Handle:
https://hdl.handle.net/2376/6113
Abstract
In 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=jca
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Details
- Title
- A conjugate gradient-neural network technique for ultrasound inverse imaging
- Creators
- Shira L. Broschat (Author)Xiaodong Zhang (Author)Patrick J. Flynn (Author)
- Publication Details
- Journal of computational acoustics., Vol.10(2), pp.243-264
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- World Scientific Publishing
- Identifiers
- 99900502729401842
- Copyright
- In copyright ; openAccess ; http://rightsstatements.org/vocab/InC/1.0/ ; http://purl.org/eprint/accessRights/OpenAccess
- Language
- English
- Resource Type
- Journal article