Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

Abstract Background To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. Methods Two phantoms were scanned with a rapid k...

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Bibliographic Details
Main Authors: Djamel Dabli, Maeliss Loisy, Julien Frandon, Fabien de Oliveira, Azhar Mohamad Meerun, Boris Guiu, Jean-Paul Beregi, Joël Greffier
Format: Article
Language:English
Published: SpringerOpen 2023-01-01
Series:European Radiology Experimental
Subjects:
Abdomen
Contrast media
Deep learning
Image processing (computer assisted)
Phantoms (imaging)
Online Access:https://doi.org/10.1186/s41747-022-00314-9

Internet

https://doi.org/10.1186/s41747-022-00314-9

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