基于蝶形空洞几何蒸馏的磁共振成像重建

doi:10.1007/s12204-024-2701-8

摘要/Abstract

摘要： 为了提高磁共振成像的重建精度，提出了一种精确的自然图像压缩感知 (Compressed Sensing， CS)重建网络，结合了基于模型和基于深度学习的CS-MRI 方法的优点。理论上，在线性重建中增强几何纹理细节是可行的。首先，将优化问题分解为线性近似和几何补偿两个问题。针对图像线性近似问题，采用数据一致性模块对其进行处理。由于处理过程会丢失纹理细节，因此提出一种显式结合图像和频率特征表示的神经网络层，命名为蝶形空洞几何蒸馏网络。该网络引入了蝶形运算的思想，巧妙地融合了图像域和频域的特征，避免了单一域提取特征时纹理细节的丢失。最后，结合通道注意力机制和空洞卷积设计了通道特征融合模块。通道注意力使得最终输出的特征图集中在更重要的部分，从而提高了特征表示能力。空洞卷积扩大了感受野，从而获得更稠密的图像特征数据。实验结果表明，在笛卡尔采样掩码CS比例为10%的大脑数据集上，该网络的峰值信噪比比 ISTA-Net+、FISTA 和DGDN 网络分别提高了 5.43 dB、5.23 dB 和 3.89 dB。

关键词: 蝶形几何蒸馏, 空洞卷积, 通道注意力, 图像重建

Abstract: In order to improve the reconstruction accuracy of magnetic resonance imaging (MRI), an accurate natural image compressed sensing (CS) reconstruction network is proposed, which combines the advantages of model-based and deep learning-based CS-MRI methods. In theory, enhancing geometric texture details in linear reconstruction is possible. First, the optimization problem is decomposed into two problems: linear approximation and geometric compensation. Aimed at the problem of image linear approximation, the data consistency module is used to deal with it. Since the processing process will lose texture details, a neural network layer that explicitly combines image and frequency feature representation is proposed, which is named butterfly dilated geometric distillation network. The network introduces the idea of butterfly operation, skillfully integrates the features of image domain and frequency domain, and avoids the loss of texture details when extracting features in a single domain. Finally, a channel feature fusion module is designed by combining channel attention mechanism and dilated convolution. The attention of the channel makes the final output feature map focus on the more important part, thus improving the feature representation ability. The dilated convolution enlarges the receptive field, thereby obtaining more dense image feature data. The experimental results show that the peak signal-tonoise ratio of the network is 5.43 dB, 5.24 dB and 3.89 dB higher than that of ISTA-Net+, FISTA and DGDN networks on the brain data set with a Cartesian sampling mask CS ratio of 10%.

中图分类号:

TP391
R445

. 基于蝶形空洞几何蒸馏的磁共振成像重建[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 591-599.

Duolin, Xu Boyu, Ren Yong, Yang Xin. Magnetic Resonance Imaging Reconstruction Based on Butterfly Dilated Geometric Distillation[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 591-599.

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