Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.

Li Z, Cai A, Wang L, Zhang W, Tang C, Li L, Liang N, Yan B

DOI: 10.3390/s19183941
Published: 2019 Sep 12
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s19183941,
  title = {Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.},
  author = {Li Z and Cai A and Wang L and Zhang W and Tang C and Li L and Liang N and Yan B},
  year = {2019},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s19183941},
  url = {https://doi.org/10.3390/s19183941}
}

RIS

TY  - JOUR
TI  - Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.
AU  - Li Z
AU  - Cai A
AU  - Wang L
AU  - Zhang W
AU  - Tang C
AU  - Li L
AU  - Liang N
AU  - Yan B
PY  - 2019
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s19183941
UR  - https://doi.org/10.3390/s19183941
ER  -

APA

Z, L., A, C., L, W., W, Z., C, T., L, L., N, L., & B, Y. (2019). Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s19183941

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pubmed · retrieved 2026-09-25T02:09:48.801Z