Deep learning-based magnetic resonance image super-resolution: a survey

被引：1
作者：
Ji Z. [1 ,2 ]
Zou B. [1 ,2 ]
Kui X. [1 ,2 ]
Liu J. [3 ]
Zhao W. [3 ]
Zhu C. [1 ,2 ,4 ]
Dai P. [1 ,2 ]
Dai Y. [1 ,2 ]
机构：
[1] School of Computer Science and Engineering, Central South University, Changsha
[2] Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha
[3] Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha
[4] The College of Literature and Journalism, Central South University, Changsha
来源：
Neural Computing and Applications | 2024年 / 36卷 / 21期
基金：
中国国家自然科学基金;
关键词：
Convolutional neural networks; Deep learning; Image super-resolution; Magnetic resonance imaging; Transformer;
D O I：
10.1007/s00521-024-09890-w
中图分类号：
学科分类号：
摘要：
Magnetic resonance imaging (MRI) is a medical imaging technique used to show anatomical structures and physiological processes of the human body. Due to limitations like image acquisition time, hardware capabilities, or uncooperative patients, the resolution of MR images is insufficient. Super-resolution (SR) is a crucial method to enhance the resolution of images without expensive scanning equipment. Recent years have witnessed significant progress in MR image super-resolution. Therefore, this survey presents a thorough overview of current developments in deep learning-based MR image super-resolution methods. In general, we can roughly divide the MRI super-resolution methods into single-contrast MR image SR methods and multi-contrast MR image SR methods. Additionally, we introduce the multi-task learning approaches about the MR image super-resolution. We also summarize other crucial topics, such as the degradation model, the definition of the super-resolution problem, the dataset, loss functions, and image quality assessment. Lastly, we indicate the challenges in the field of super-resolution and draw a conclusion to our survey. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
引用
收藏
页码：12725 / 12752
页数：27
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