融合注意力机制的多尺度残差Unet的磁共振图像重建

doi:10.11938/cjmr20223040

摘要/Abstract

摘要：

为了提高磁共振图像在欠采样下重建的质量，本文融合注意力机制和多尺度残差卷积构建Unet网络，实现磁共振图像在欠采样下的重建算法．为增强网络特征的表现能力，以及防止网络训练中梯度消失与退化的问题，在Unet网络的编码路径中引入多尺度残差卷积，提取不同尺度的特征信息；为能准确地恢复图像的细节纹理特征，在Unet网络编码和解码路径的跳层拼接部分引入卷积注意力块，对细节纹理等关键信息进行不同程度的响应．实验表明，本文方法可通过欠采样k-空间数据快速重建出细节纹理清晰且无重叠伪影的高质量磁共振图像．

关键词: 磁共振成像, 图像重建, Unet, 注意力机制, 深度学习

Abstract:

This paper integrates the attention mechanism and multi-scale residual convolution to construct a Unet network, aiming at improving the quality of magnetic resonance image (MRI) reconstructed from under-sampled k-space data. To enhance the feature representation ability of the network and prevent gradient disappearance and degradation during network training, multi-scale residual convolution was embedded in the encoding path of the Unet network to extract different scale feature information of MRI. Moreover, to accurately recover the detailed texture features of MRI, the convolution attention module was embedded in the jump connection part between the encoding and decoding path of the Unet network to respond to the key information, such as details and textures in different degrees. Experiments showed that the proposed network could effectively reconstruct high-quality MRIs with clear texture and without overlapping artifacts from the under-sampled k-space data.

Key words: magnetic resonance imaging, image reconstruction, Unet, attention mechanism, deep learning

中图分类号:

TP391.41

李奕洁, 杨馨雨, 杨晓梅. 融合注意力机制的多尺度残差Unet的磁共振图像重建[J]. 波谱学杂志, 2023, 40(3): 307-319.

Li Yijie, YANG Xinyu, YANG Xiaomei. Magnetic Resonance Image Reconstruction of Multi-scale Residual Unet Fused with Attention Mechanism[J]. Chinese Journal of Magnetic Resonance, 2023, 40(3): 307-319.

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网络模型	PSNR			SSIM
网络模型	×2	×3	×4	×2	×3	×4
PD-net	32.5438±1.0981	30.9821±1.1230	30.6958±1.0923	0.7736±0.0812	0.6802±0.0800	0.6345±0.0813
Cascade-net	31.5583±1.1120	30.1253±1.096	29.8148±1.0220	0.7651±0.0795	0.6752±0.0820	0.6340±0.0760
KIKI-net	30.7728±1.4592	28.6321±1.3201	29.7129±1.5329	0.7532±0.0821	0.6457±0.0832	0.6274±0.0846
DC-WCNN	32.8576±1.0786	30.9972±1.2356	30.7214±1.0643	0.7821±0.0831	0.6853±0.0801	0.6399±0.0842
AttMRes-Unet	33.2185±1.2759	31.7255±1.1794	30.9175±1.2376	0.7862±0.0862	0.6972±0.0859	0.6497±0.0865

网络模型	参数量	训练时间/s	重建时间/ms
PD-net	318280	9024	487.5
Cascade-net	424570	3597	70.1
KIKI-net	890504	6302	186.8
DC-WCNN	534080	4809	32.9
AttMRes-Unet	404714	2301	21.1

网络模型	PSNR			SSIM
网络模型	×2	×3	×4	×2	×3	×4
Unet	32.5193±1.0901	31.3347±1.0899	30.8267±1.0921	0.7771±0.0761	0.6829±0.0770	0.6475±0.0769
Att-Unet	32.9367±1.1096	31.5930±1.2019	30.9052±1.1099	0.7777±0.0799	0.6887±0.0811	0.6485±0.0787
MRes-Unet	33.0847±0.9998	31.6665±1.0621	30.9085±1.2260	0.7849±0.0814	0.6959±0.0816	0.6445±0.0813
SAMRes-Unet	33.1018±1.3921	31.6770±1.2998	30.9095±1.2975	0.7852±0.0824	0.6953±0.0827	0.6466±0.0825
CAMRes-Unet	33.1115±1.3001	31.6801±1.2645	30.9106±1.2301	0.7856±0.0831	0.6960±0.0829	0.6476±0.0833
AttMRes-Unet	33.2185±1.2759	31.7255±1.1794	30.9175±1.2376	0.7862±0.0862	0.6972±0.0859	0.6497±0.0865

网络模型	参数量	训练时间/s	重建时间/ms
Unet	485065	2498	19.4
Att-Unet	490730	2506	20.6
MRes-Unet	399049	2240	19.9
SAMRes-Unet	399058	2253	20.1
CAMRes-Unet	404705	2289	20.2
AttMRes-Unet	404714	2301	21.1