Cargando…
Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images
In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep netw...
Autores principales: | Zhu, Linlin, Han, Yu, Xi, Xiaoqi, Li, Lei, Yan, Bin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708215/ https://www.ncbi.nlm.nih.gov/pubmed/34960258 http://dx.doi.org/10.3390/s21248164 |
Ejemplares similares
-
Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction
por: Peng, Chengtao, et al.
Publicado: (2017) -
Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting
por: Hegazy, Mohamed A. A., et al.
Publicado: (2023) -
Metal Artifact Reduction in Dental Computed Tomography Images Based on Sinogram Segmentation Using Curvelet Transform Followed by Hough Transform
por: Yazdi, Mehran, et al.
Publicado: (2017) -
Metal artifact reduction on cervical CT images by deep residual learning
por: Huang, Xia, et al.
Publicado: (2018) -
Reduction of metal artifacts from knee tumor prostheses on CT images: value of the single energy metal artifact reduction (SEMAR) algorithm
por: Zhang, Fang-ling, et al.
Publicado: (2021)