Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose

Autor:	Lingming, Zeng, Xu, Xu, Wen, Zeng, Wanlin, Peng, Jinge, Zhang, Hu, Sixian, Keling, Liu, Chunchao, Xia, Zhenlin, Li
Rok vydání:	2020
Předmět:	Adult Technology Deep Learning Liver Humans Radiographic Image Interpretation Computer-Assisted Radiation Dosage Tomography X-Ray Computed Algorithms
Zdroj:	European journal of radiology. 135
ISSN:	1872-7727
Popis:	This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT).This study enrolled 207 adults, and they were divided into two groups: SDCT and low-dose CT (LDCT). SDCT was reconstructed using the HIR method (SDCTThe mean effective doses were 5.64 ± 1.96 mSv for SDCT and 2.87 ± 0.87 mSv for LDCT. The noise of LDCTLDCT with DELTA had approximately 49 % dose reduction compared with SDCT with HIR while maintaining image quality on contrast-enhanced liver CT.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::712871485c5d1cebcc1574c413805a0d https://pubmed.ncbi.nlm.nih.gov/33418383 Zobrazit plný text záznamu Full Text from ScienceDirect