The application of the generalized vector sample pattern matching method for EIT image reconstruction

Bayford, Richard; Gao, Shangkai; Saito, Yoshifuru; Yerworth, Rebecca J.; Holder, David; Yan, Weili

doi:10.1088/0967-3334/24/2/356

Cited by 8 publications

(14 citation statements)

References 20 publications

(25 reference statements)

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“…The 3D mesh system used in this paper is the same as that in Dong et al (2003). The mesh generated for the spherical model consists of 18 131 tetrahedral primary elements and 3514 nodes.…”

Section: Modeled Mesh Systemmentioning

confidence: 99%

“…The 3D spherical tank data used in this paper are also the same as that in Dong et al (2003). The 3D spherical tank (figure 1) was made from two Perspex bowls with 19 cm inner diameter.…”

Section: Tank Datamentioning

confidence: 99%

“…Coupled with a Loreta solution as the initial estimation, FOCUSS has been used previously for improving the spatial resolution and localization in reconstructions of EEG source distributions (Liu 2003). In order to improve the spatial resolution of EIT images by the FOCUSS method, combined with the generalized vector sample pattern matching method (GVSPM) (Dong et al 2003), it was successfully applied to improve the spatial resolution and localization precision of two-dimensional (2D) EIT images from simulated and tank data, for a model of a 2D homogeneous circular disc (Dong et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…In 2D EIT, GVSPM can obtain stable solutions and smoother reconstructed images which has been successfully used as the initial value of FOCUSS. But for 3D reconstruction, there was a significant increase in the number of image artifacts, this was due to the higher ratio of mesh elements as compared with the number of boundary measurements, and the convergence speed is slow, caused by an error in the electrode positions which may be overcome by increasing the mesh density under the electrodes (Dong et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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Spatial resolution improvement of 3D EIT images by the shrinking sLORETA-FOCUSS algorithm

et al. 2005

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This paper describes the use of the shrinking sLORETA-FOCUSS algorithm to improve the spatial resolution of three-dimensional (3D) EIT images. Conventional EIT yields inaccurate, low spatial resolution images, due to noise, the low sensitivity of boundary voltages to inner conductivity perturbations and a limited number of boundary voltage measurements. The focal underdetermined system solver (FOCUSS) algorithm produces a localized energy solution based on the weighted minimum-norm least-squares (MNLS) solution. It was successfully applied for the spatial resolution improvement of EIT images of simulated and tank data for a 2D homogeneous circular disc. However, due to the fact that a 3D mesh system contains many more elements, much more memory is required to store the weighting matrix. In order to extend the work to 3D, the shrinking-FOCUSS method is utilized to shrink the solution space as well as the weighting matrix in each iteration step. The solution of the standardized low resolution electromagnetic tomography algorithm (sLORETA) is adopted as the initial estimate of the shrinking-FOCUSS. The effectiveness is verified by implementing the new algorithm on tank data for a three-dimensional homogeneous sphere.

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“…The 3D mesh system used in this paper is the same as that in Dong et al (2003). The mesh generated for the spherical model consists of 18 131 tetrahedral primary elements and 3514 nodes.…”

Section: Modeled Mesh Systemmentioning

confidence: 99%

“…The 3D spherical tank data used in this paper are also the same as that in Dong et al (2003). The 3D spherical tank (figure 1) was made from two Perspex bowls with 19 cm inner diameter.…”

Section: Tank Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial resolution improvement of 3D EIT images by the shrinking sLORETA-FOCUSS algorithm

et al. 2005

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“…The key idea of the GVSPM is that the objective function is the angle obtained from the inner product between the input vector and the solution of a system of equations. GVSPM has been widely utilized to solve the ill-posed inverse problem, and it has many applications such as the images of yeast cell sedimentary distribution in a multilayered microchannel [24], solid-air two-phase flow [25], and 3D images for Perspex [26]. The recently proposed EIT research is used to monitor patients with COVID-19 [27]- [28] and measure the postoperative lung volumes [29].…”

Section: Introductionmentioning

confidence: 99%