Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the...

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Bibliographic Details
Main Authors: Kyeong Hwa Ryu, Hye Jin Baek, Sung-Min Gho, Kanghyun Ryu, Dong-Hyun Kim, Sung Eun Park, Ji Young Ha, Soo Buem Cho, Joon Sung Lee
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Journal of Clinical Medicine
Subjects:
neural networks (computer)
deep learning
image enhancement
magnetic resonance imaging
image interpretation
computer-assisted
Online Access:https://www.mdpi.com/2077-0383/9/2/364

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https://www.mdpi.com/2077-0383/9/2/364

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