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A robust image reconstruction algorithm and its parallel implementation in electrical impedance tomography

Tipo de material: TextoTextoSeries ; IEEE Trans Med Imaging., 12(2), p.137-146, 1993Trabajos contenidos:
  • Woo Ej
  • Hua P
  • Webster Jg
  • Tompkins Wj
Tema(s): Recursos en línea: Resumen: An efficient and robust image reconstruction algorithm for static impedance imaging using Hachtel's augmented matrix method was developed. This improved Newton-Raphson method produced more accurate images by reducing the undesirable effects of the ill-conditioned Hessian matrix. It is demonstrated that the electrical impedance tomography (EIT)system could produce two-dimensional static images from a physical phantom with 7 percent spatial resolution at the center and 5 percent at the periphery. Static EIT image reconstruction requires a large amount of computation. In order to overcome the limitations on reducing the computation time by algorithmic approaches, the improved Newton-Raphson algorithm was implemented on a parallel computer system. It is shown that the parallel computation could reduce the computation time from hours to minutes.
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Documentos solicitados Documentos solicitados CICY Documento préstamo interbibliotecario Ref1 B-16424 (Browse shelf(Opens below)) Available

An efficient and robust image reconstruction algorithm for static impedance imaging using Hachtel's augmented matrix method was developed. This improved Newton-Raphson method produced more accurate images by reducing the undesirable effects of the ill-conditioned Hessian matrix. It is demonstrated that the electrical impedance tomography (EIT)system could produce two-dimensional static images from a physical phantom with 7 percent spatial resolution at the center and 5 percent at the periphery. Static EIT image reconstruction requires a large amount of computation. In order to overcome the limitations on reducing the computation time by algorithmic approaches, the improved Newton-Raphson algorithm was implemented on a parallel computer system. It is shown that the parallel computation could reduce the computation time from hours to minutes.

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