| 基于3MNet去噪网络的快速EPRI成像 |
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| 引用本文： | 钱佩璋,乔志伟,杜聪聪.基于3MNet去噪网络的快速EPRI成像[J].CT理论与应用研究,2023,32(1):55-66. |
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| 作者姓名： | 钱佩璋 乔志伟 杜聪聪 |
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| 作者单位： | 山西大学计算机与信息技术学院, 太原 030006 |
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| 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））。 |
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| 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。
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| 关 键 词： | 深度学习 卷积神经网络 电子顺磁共振成像 快速成像 |
| 收稿时间： | 2022-04-15 |
Fast EPRI Imaging Based on 3MNet Denoising Network |
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| Institution: | School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China |
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| 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. |
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