Published May 2022 | Version v1
Journal article Metadata-only

Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks

Creators

  • 1. Department of Radiology, Kumamoto University Hospital (Japan)
  • 2. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University (Japan)

Description

The aim of this study was to generate virtual Magnetic resonance (MR) from computed tomography (CT) using conditional generative adversarial networks (cGAN).

Availability note (English)

Available from http://dx.doi.org/10.1016/j.radi.2021.10.006

Additional details

Identifiers

DOI
10.1016/j.radi.2021.10.006;
PII
S1078817421001668;

Publishing Information

Journal Title
Radiography (London 1995)
Journal Volume
28
Journal Issue
2
Journal Page Range
vp.
ISSN
1078-8174

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53123972
Subject category
S62: RADIOLOGY AND NUCLEAR MEDICINE;
Descriptors DEI
COMPUTERIZED TOMOGRAPHY; IMAGE PROCESSING; IMAGES; MACHINE LEARNING; NMR IMAGING; VERTEBRAE
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; BODY; DIAGNOSTIC TECHNIQUES; LEARNING; MATHEMATICAL LOGIC; ORGANS; PROCESSING; SKELETON; TOMOGRAPHY

Optional Information

Copyright
Copyright (c) 2021 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.