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: Afat, S. (Author), Brendlin, A.S (Author), Chaika, M. (Author), Esser, M. (Author), Estler, A. (Author), Mader, M. (Author), Männlin, S. (Author), Plajer, D. (Author), Schäfer, J. (Author), Schmid, U. (Author), Spogis, J. (Author), Tsiflikas, I. (Author), Wrazidlo, R. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
AI (artificial intelligence)
computed tomography
image quality enhancement
pneumonia
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