A residual dense network assisted sparse view reconstruction for breast computed tomography

To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Pro...

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Detalles Bibliográficos
Autores principales: Fu, Zhiyang, Tseng, Hsin Wu, Vedantham, Srinivasan, Karellas, Andrew, Bilgin, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713379/
https://www.ncbi.nlm.nih.gov/pubmed/33273541
http://dx.doi.org/10.1038/s41598-020-77923-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713379/
https://www.ncbi.nlm.nih.gov/pubmed/33273541
http://dx.doi.org/10.1038/s41598-020-77923-0

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