Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning

Xia Hao; Cai Nian; Wang Ping; Wang Han

doi:10.12052/gdutxb.200011

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Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning

更新时间：2026-05-27

- Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning
- Vol. 37, Issue 6, Pages: 26-31(2020)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 广州医科大学附属第一医院,广东,广州,510120
  3. 广东工业大学机电工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200011
  CLC： TP391.4
- Online First：02 November 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
夏皓, 蔡念, 王平, 王晗. 基于多分辨率学习卷积神经网络的磁共振图像超分辨率重建[J]. 广东工业大学学报, 2020, 37(6): 26-31.

Xia Hao, Cai Nian, Wang Ping, et al. Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning[J]. 2020, 37(6): 26-31.
夏皓, 蔡念, 王平, 王晗. 基于多分辨率学习卷积神经网络的磁共振图像超分辨率重建[J]. 广东工业大学学报, 2020, 37(6): 26-31. DOI： 10.12052/gdutxb.200011.

Xia Hao, Cai Nian, Wang Ping, et al. Magnetic Resonance Image Super-Resolution via Multi-Resolution Learning[J]. 2020, 37(6): 26-31. DOI： 10.12052/gdutxb.200011.

摘要

高分辨率磁共振图像对于医学诊断具有重要意义

本文提出一种多分辨率学习卷积神经网络

并应用于磁共振图像超分辨率。网络是一种新型深度残差网络

包含用于特征提取的残差单元、多分辨率上采样的反卷积层以及多分辨率学习层。设计的网络在低分辨率图像空间中实现图像超分辨率

采用多分辨率上采样实现多个残差单元信息融合并加速网络

多分辨率学习能够自适应地确定各分辨率上采样的高维特征图对磁共振图像超分辨重建的贡献度。实验表明

论文提出的方法能够很好地超分辨率重建磁共振图像

优于最新的深度学习方法。

Abstract

High-resolution magnetic resonance images are of great significance for medical diagnosis. A convolutional neural network with multi-resolution learning is proposed for magnetic resonance image (MR) super-resolution. The network is an improved deep residual network

which involves residual units for feature extraction

a deconvolution layer for multi-resolution up-sampling

and a multi-resolution learning layer. The proposed network performs the super-resolution task in the low-resolution space

which can accelerate the network. Multi-resolution upsampling is put forward to integrate multiple residual unit information and to accelerate the network. Multi-resolution learning can adaptively determine the contributions of these upsampled high-dimensional feature maps to high-resolution MR image reconstruction. Experiment results indicate that the proposed method can achieve a good super-resolution reconstruction performance for magnetic resonance images

which is superior to the state-of-the-art deep learning methods.

关键词

Keywords

references

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