A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond

We present a joint model based on deep learning that is designed to inpaint the missing-wedge sinogram of electron tomography and reduce the residual artifacts in the reconstructed tomograms. Traditional methods, such as weighted back projection (WBP) and simultaneous algebraic reconstruction techni...

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Detalles Bibliográficos
Autores principales: Ding, Guanglei, Liu, Yitong, Zhang, Rui, Xin, Huolin L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6728317/
https://www.ncbi.nlm.nih.gov/pubmed/31488874
http://dx.doi.org/10.1038/s41598-019-49267-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6728317/
https://www.ncbi.nlm.nih.gov/pubmed/31488874
http://dx.doi.org/10.1038/s41598-019-49267-x

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