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Titel:

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

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Ries, Annika; Dorosti, Tina; Thalhammer, Johannes; Sasse, Daniel; Sauter, Andreas; Meurer, Felix; Benne, Ashley; Lasser, Tobias; Pfeiffer, Franz; Schaff, Florian; Pfeiffer, Daniela
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 (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subse...     »
Zeitschriftentitel:
Eur Radiol Exp
Jahr:
2024
Band / Volume:
8
Heft / Issue:
1
Volltext / DOI:
doi:10.1186/s41747-024-00450-4
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38698099
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski)
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