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
相关论文
共 50 条
  • [41] Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning
    Ordinola, Alfredo
    Abramian, David
    Herberthson, Magnus
    Eklund, Anders
    Ozarslan, Evren
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [42] Deep-Learning Super-Resolution MRI: Getting Something From Nothing
    Chong, Jaron J. R.
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 51 (04) : 1140 - 1141
  • [43] Super-resolution imaging goes fast and deep
    Sam Duwé
    Peter Dedecker
    Nature Methods, 2017, 14 : 1042 - 1044
  • [44] Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network
    Song, Xibin
    Dai, Yuchao
    Qin, Xueying
    COMPUTER VISION - ACCV 2016, PT IV, 2017, 10114 : 360 - 376
  • [45] Super-resolution imaging goes fast and deep
    Duwe, Sam
    Dedecker, Peter
    NATURE METHODS, 2017, 14 (11) : 1041 - 1044
  • [46] A super-resolution strategy for mass spectrometry imaging via transfer learning
    Liao, Tiepeng
    Ren, Zihao
    Chai, Zhaoliang
    Yuan, Man
    Miao, Chenjian
    Li, Junjie
    Chen, Qi
    Li, Zhilin
    Wang, Ziyi
    Yi, Lin
    Ge, Siyuan
    Qian, Wenwei
    Shen, Longfeng
    Wang, Zilei
    Xiong, Wei
    Zhu, Hongying
    NATURE MACHINE INTELLIGENCE, 2023, 5 (06) : 656 - +
  • [47] A super-resolution strategy for mass spectrometry imaging via transfer learning
    Tiepeng Liao
    Zihao Ren
    Zhaoliang Chai
    Man Yuan
    Chenjian Miao
    Junjie Li
    Qi Chen
    Zhilin Li
    Ziyi Wang
    Lin Yi
    Siyuan Ge
    Wenwei Qian
    Longfeng Shen
    Zilei Wang
    Wei Xiong
    Hongying Zhu
    Nature Machine Intelligence, 2023, 5 : 656 - 668
  • [48] A Survey of Deep Learning Video Super-Resolution
    Baniya, Arbind Agrahari
    Lee, Tsz-Kwan
    Eklund, Peter W.
    Aryal, Sunil
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2024, 8 (04): : 2655 - 2676
  • [49] Deep learning for super-resolution localization microscopy
    Zhou, Tianyang
    Luo, Jianwen
    Liu, Xin
    OPTICS IN HEALTH CARE AND BIOMEDICAL OPTICS VIII, 2018, 10820
  • [50] Deep Learning for Image Super-Resolution: A Survey
    Wang, Zhihao
    Chen, Jian
    Hoi, Steven C. H.
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (10) : 3365 - 3387