Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions

This study aimed to compare the image quality and diagnostic accuracy of deep-learning-based image denoising reconstructions (DLIDs) to established iterative reconstructed algorithms in low-dose computed tomography (LDCT) of patients with suspected urolithiasis. LDCTs (CTDIvol, 2 mGy) of 76 patients...

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Detalles Bibliográficos
Autores principales: Terzis, Robert, Reimer, Robert Peter, Nelles, Christian, Celik, Erkan, Caldeira, Liliana, Heidenreich, Axel, Storz, Enno, Maintz, David, Zopfs, David, Große Hokamp, Nils
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486912/
https://www.ncbi.nlm.nih.gov/pubmed/37685359
http://dx.doi.org/10.3390/diagnostics13172821

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486912/
https://www.ncbi.nlm.nih.gov/pubmed/37685359
http://dx.doi.org/10.3390/diagnostics13172821

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