AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans

AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans

(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection...

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Bibliographic Details
Main Authors: Andreas S. Brendlin, Ulrich Schmid, David Plajer, Maryanna Chaika, Markus Mader, Robin Wrazidlo, Simon Männlin, Jakob Spogis, Arne Estler, Michael Esser, Jürgen Schäfer, Saif Afat, Ilias Tsiflikas
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Tomography
Subjects:
pneumonia
computed tomography
AI (artificial intelligence)
image quality enhancement
Online Access:https://www.mdpi.com/2379-139X/8/4/140

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