A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G...

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Bibliographic Details
Main Author: Krzysztof Malczewski
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Applied Sciences
Subjects:
MRI
GAN
generative models
super-resolution
magnetic resonance imaging
compressed sensing
Online Access:https://www.mdpi.com/2076-3417/14/4/1351

Internet

https://www.mdpi.com/2076-3417/14/4/1351

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