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

被引:0
|
作者
Li, Yunhe [1 ]
Yang, Mei [1 ]
Bian, Tao [1 ]
Wu, Haitao [2 ]
机构
[1] Zhaoqing Univ, Sch Elect & Elect Engn, Zhaoqing 526060, Peoples R China
[2] Shenzhen CZTEK Co Ltd, Shenzhen 518055, Peoples R China
关键词
magnetic resonance imaging; super-resolution; generative adversarial networks;
D O I
10.3390/info15100655
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:20
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