MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI ima...

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Bibliographic Details
Published inInformation (Basel) Vol. 15; no. 10; p. 655
Main Authors Li, Yunhe, Yang, Mei, Bian, Tao, Wu, Haitao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2024
Subjects
Clinical medicine
Computational linguistics
Datasets
Deep learning
Design
Generative adversarial networks
Image enhancement
Image quality
Image resolution
Language processing
Magnetic resonance imaging
Medical imaging
Medical imaging equipment
Natural language interfaces
Quality assessment
super-resolution
Visual perception
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ISSN2078-2489
2078-2489
DOI10.3390/info15100655

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Summary:This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info15100655