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A deep learning reconstruction framework for X-ray computed tomography with incomplete data
As a powerful imaging tool, X-ray computed tomography (CT) allows us to investigate the inner structures of specimens in a quantitative and nondestructive way. Limited by the implementation conditions, CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not...
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| Publicado en: | PLoS One |
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| Autores principales: | , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Public Library of Science
2019
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6824569/ https://ncbi.nlm.nih.gov/pubmed/31675363 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0224426 |
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