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

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 <i>data challenge</i> that we organized, bringing togeth...

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Bibliographic Details
Main Authors: Johannes Leuschner, Maximilian Schmidt, Poulami Somanya Ganguly, Vladyslav Andriiashen, Sophia Bethany Coban, Alexander Denker, Dominik Bauer, Amir Hadjifaradji, Kees Joost Batenburg, Peter Maass, Maureen van Eijnatten
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Journal of Imaging
Subjects:
computed tomography (CT)
image reconstruction
low-dose
sparse-angle
deep learning
quantitative comparison
Online Access:https://www.mdpi.com/2313-433X/7/3/44

Internet

https://www.mdpi.com/2313-433X/7/3/44

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