Improving image quality of sparse-view lung tumor CT images with U-Net

Abstract Background We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. Methods CT images from 41 subjects aged 62.8 ± 10.6 years...

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出版年:	European Radiology Experimental
主要な著者:	Annika Ries, Tina Dorosti, Johannes Thalhammer, Daniel Sasse, Andreas Sauter, Felix Meurer, Ashley Benne, Tobias Lasser, Franz Pfeiffer, Florian Schaff, Daniela Pfeiffer
フォーマット:	論文
言語:	英語
出版事項:	SpringerOpen 2024-05-01
主題:	Artifacts Image processing (computer-assisted) Lung metastasis Neural networks (computer) Tomography (x-ray computed)
オンライン･アクセス:	https://doi.org/10.1186/s41747-024-00450-4