Published February 2021
| Version v1
Journal article
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
Creators
- 1. Department of Radiology, West China Hospital, Sichuan University, Chengdu (China)
Description
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).
Availability note (English)
Available from http://dx.doi.org/10.1016/j.ejrad.2020.109487Additional details
Identifiers
- DOI
- 10.1016/j.ejrad.2020.109487;
- PII
- S0720048X2030677X;
Publishing Information
- Journal Title
- European Journal of Radiology
- Journal Volume
- 135
- Journal Page Range
- vp.
- ISSN
- 0720-048X
- CODEN
- EJRADR
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53110566
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- COMPARATIVE EVALUATIONS; COMPUTERIZED TOMOGRAPHY; DOSES; HYBRIDIZATION; IMAGE PROCESSING; ITERATIVE METHODS; LIVER; MACHINE LEARNING
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; BODY; CALCULATION METHODS; DIAGNOSTIC TECHNIQUES; DIGESTIVE SYSTEM; EVALUATION; GLANDS; LEARNING; MATHEMATICAL LOGIC; ORGANS; PROCESSING; TOMOGRAPHY
Optional Information
- Copyright
- Copyright (c) 2020 Elsevier B.V. All rights reserved.