基于3MNet去噪网络的快速EPRI成像

钱佩璋; 乔志伟; 杜聪聪

doi:10.15953/j.ctta.2022.065

基于3MNet去噪网络的快速EPRI成像

doi: 10.15953/j.ctta.2022.065

山西大学计算机与信息技术学院, 太原 030006

基金项目: 国家自然科学基金面上项目（模型与数据耦合驱动的快速四维EPRI肿瘤氧成像（62071281））；中央引导地方科技发展资金项目（新型TV和学习先验联合约束的快速四维EPRI成像方法（YDZJSX2021A003））；山西省回国留学人员科研资助项目（基于新型四维TV正则机理的快速EPRI肿瘤氧成像方法研究（2020-008））。

详细信息

作者简介:
钱佩璋：男，山西大学计算机技术专业硕士研究生，主要从事医学图像重建、图像处理等方面的研究，E-mail：1124792859@qq.com

乔志伟：男，博士，山西大学计算机与信息技术学院教授、博士生导师，主要从事电子顺磁共振成像、图像重建算法、高性能计算等方面的研究，E-mail：zqiao@sxu.edu.cn

通讯作者:
乔志伟

中图分类号: O 242；TP 391.41
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出版历程
- 收稿日期: 2022-04-15
- 录用日期: 2022-05-09
- 网络出版日期: 2022-05-16
- 刊出日期: 2023-01-31

Fast EPRI Imaging Based on 3MNet Denoising Network

School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China

摘要

摘要: 电子顺磁共振成像（EPRI）是一种先进的氧成像技术。当前EPRI的瓶颈问题是扫描速度过慢，其原因是每个角度下的投影信号需要被重复采集几千次，以压制随机噪声。一种实现快速扫描的方法是减少投影信号的重复采集次数，然而这又使得投影信号信噪比降低，重建出来的图像噪声较大。为有效压制重建图像中的噪声，本文提出一种基于多通道、多尺度、多拼接的（3MNet）图像去噪网络，以实现高精度快速EPRI成像。该网络由3个子网络构成。第1个子网络是基于注意力机制的卷积网络，其输出的特征图像与输入图像拼接以构成后端网络的输入；第2个子网络是3通道卷积网络；第3个子网络是多尺度卷积网络。实验结果表明，本文提出的3MNet网络可以实现EPRI图像的高精度去噪，进而实现快速成像。
- 深度学习 /
- 卷积神经网络 /
- 电子顺磁共振成像 /
- 快速成像
Abstract: Electron paramagnetic resonance imaging (EPRI) is an advanced technique for oxygen imaging. The current bottleneck in EPRI is the slow scanning speed, due to the fact that the projection signal at each angle needs to be repeated thousands of times to suppress random noise. One way to achieve fast scanning is to reduce the number of repeated projection signal acquisitions; however, this, in turn, reduces the signal-to-noise ratio of the projection signal and results in a noisy reconstructed image. To effectively suppress the noise in the reconstructed image, this study proposes a multi-channel, multi-scale, multi-concatenation, and convolutional network (3MNet) based image denoising network to achieve high accuracy and fast EPRI imaging. The proposed network consists of three sub-networks. The first sub-network is an attention-based convolutional network, whose output feature images are stitched with the input images to form the input of the back-end network. The second sub-network is a three-channel convolutional network. Finally, the third sub-network is a multi-scale convolutional network. The experimental results demonstrate that the proposed 3MNet network can achieve high accuracy in denoising EPRI images and fast imaging.
- deep learning /
- convolutional neural network /
- EPRI /
- rapid imaging

HTML全文

图 1 CNN去噪示意图

Figure 1. CNN denoising schematic diagram

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图 2 3MNet网络结构图

Figure 2. 3MNet network structure diagram

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图 3 双重注意力模块

Figure 3. Dual attention module

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图 4 残差密集连接块

Figure 4. Residual dense connecting blocks

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图 5 空洞卷积模块

Figure 5. Dilated convolution module

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图 6 通道注意力模块

Figure 6. Channel attention module

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图 7 多尺度特征融合模块

Figure 7. Multi-scale feature fusion module

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图 8 EPRI氧成像仪示意图

Figure 8. Schematic of the EPRI oxygen imager

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图 9 EPRI小瓶子示意图

Figure 9. Schematic of EPRI small bottles

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图 10 EPRI图像示意图

Figure 10. Schematic of EPRI images

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图 11 去噪结果示意图

Figure 11. Diagram of denoising results

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图 12 去噪结果局部放大图

Figure 12. Local enlarged view of denoising results

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图 13 3MNet内部子网络实验对比图

Figure 13. 3MNet comparison diagram of internal sub-network experiment

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图 14 3MNet内部子网络实验局部放大图

Figure 14. 3MNet partial enlargement of internal sub-network experiment

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表 1 去噪实验对比结果

Table 1. Comparison results of denoising experiments

方法	X		Y		Z
方法	R/dB	e	R/dB	e	R/dB	e
DnCNN	82.142	1.563e-4	85.175	1.102e-4	82.234	1.547e-4
RED-CNN	83.495	1.338e-4	88.428	7.580e-5	85.440	1.070e-4
UNet	89.157	6.970e-5	89.257	6.890e-5	89.092	7.020e-5
CBDNet	92.837	4.562e-5	95.498	3.358e-5	95.235	3.462e-5
BRDNet	93.519	4.218e-5	96.901	2.858e-5	96.035	3.157e-5
PRIDNet	93.943	4.017e-5	96.284	3.068e-5	95.770	3.255e-5
3MNet	96.766	2.902e-5	97.257	2.743e-5	96.514	2.988e-5

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表 2 3 MNet内部子网络实验对比结果

Table 2. 3 MNet internal sub-network experimental comparison results

方法	R/dB	e（×10^-5）
Remove both	93.363	4.294
Remove image preprocessing	95.322	3.427
Remove multiscale convolution	95.647	3.301
3MNet	96.766	2.902

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