Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm expert...

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Detalles Bibliográficos
Autores principales: Leuschner, Johannes, Schmidt, Maximilian, Ganguly, Poulami Somanya, Andriiashen, Vladyslav, Coban, Sophia Bethany, Denker, Alexander, Bauer, Dominik, Hadjifaradji, Amir, Batenburg, Kees Joost, Maass, Peter, van Eijnatten, Maureen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321320/
https://www.ncbi.nlm.nih.gov/pubmed/34460700
http://dx.doi.org/10.3390/jimaging7030044

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321320/
https://www.ncbi.nlm.nih.gov/pubmed/34460700
http://dx.doi.org/10.3390/jimaging7030044

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