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- W4387490233 abstract "Due to the noncoincidence between the mono energetic rays-based reconstruction algorithms and the multi-energy projection data, there always exit beam hardening artifacts destroying the reconstructed CT images. A correction of the beam hardening artifacts can effectively enhance the accuracy and reliability of the scan results. In this preliminary study, we propose a pix2pixGAN generative adversarial network to do this work. Compared to the traditional approaches, this method can increase the structural similarity index measure by 11.81%, improve the decrease the peak signal-to-noise ratio by 25.59% and decrease the root-mean-square error by 62.02 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>%</sup> . These results demonstrate that the pix2pixGAN generative adversarial network can simultaneously correct the artifacts and improve the recovery of details." @default.
- W4387490233 created "2023-10-11" @default.
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- W4387490233 date "2023-08-09" @default.
- W4387490233 modified "2023-10-16" @default.
- W4387490233 title "Correction of the Beam Hardening Artifacts in CT Images Using Pix2pixGAN Network" @default.
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- W4387490233 doi "https://doi.org/10.1109/icemi59194.2023.10270262" @default.
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