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Artifact Removal Us...
Artifact Removal Using GAN Network for Limited-Angle CT Reconstruction
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- Xie, Shipeng (författare)
- Nanjing University of Posts and Telecommunications,College of Telecommunications & Information Engineering,Nanjing,China
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- Xu, Hui (författare)
- Nanjing University of Posts and Telecommunications,College of Telecommunications & Information Engineering,Nanjing,China
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- Li, Haibo (författare)
- KTH,Medieteknik och interaktionsdesign, MID
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(creator_code:org_t)
- 2019
- 2019
- Engelska.
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Ingår i: 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019.
- Relaterad länk:
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https://ieeexplore.i...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which .can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)