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

• Published in: European radiology experimental Vol. 8; no. 1; pp. 54 - 11
• Main Authors: Ries, Annika, Dorosti, Tina, Thalhammer, Johannes, Sasse, Daniel, Sauter, Andreas, Meurer, Felix, Benne, Ashley, Lasser, Tobias, Pfeiffer, Franz, Schaff, Florian, Pfeiffer, Daniela
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 03.05.2024; Springer Nature B.V; SpringerOpen
• Subjects: Aged; Diagnostic Radiology; Female; Humans; Image processing (computer-assisted); Imaging; Internal Medicine; Interventional Radiology; Lung cancer; Lung metastasis; Lung Neoplasms - diagnostic imaging; Male; Medicine; Medicine & Public Health; Metastasis; Middle Aged; Neural networks (computer); Neuroradiology; Original Article; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Retrospective Studies; Tomography, X-Ray Computed - methods; Tomography (x-ray computed); Ultrasound; Artifacts; Image processing (computer-assisted); Neural networks (computer); Tomography (x-ray computed); Lung metastasis
• ISSN: 2509-9280; 2509-9280
• DOI: 10.1186/s41747-024-00450-4

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 (mean ± s...