Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping...

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Detalles Bibliográficos
Autores principales: Gu, Peijian, Jiang, Changhui, Ji, Min, Zhang, Qiyang, Ge, Yongshuai, Liang, Dong, Liu, Xin, Yang, Yongfeng, Zheng, Hairong, Hu, Zhanli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339014/
https://www.ncbi.nlm.nih.gov/pubmed/30626109
http://dx.doi.org/10.3390/s19010207

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339014/
https://www.ncbi.nlm.nih.gov/pubmed/30626109
http://dx.doi.org/10.3390/s19010207

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