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基于3MNet去噪网络的快速EPRI成像

钱佩璋 乔志伟 杜聪聪

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. DOI: 10.15953/j.ctta.2022.065
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. DOI: 10.15953/j.ctta.2022.065
QIAN P Z, QIAO Z W, DU C C. Fast EPRI Imaging Based on 3MNet Denoising Network[J]. CT Theory and Applications, 2023, 32(1): 55-66. DOI: 10.15953/j.ctta.2022.065. (in Chinese)
Citation: QIAN P Z, QIAO Z W, DU C C. Fast EPRI Imaging Based on 3MNet Denoising Network[J]. CT Theory and Applications, 2023, 32(1): 55-66. DOI: 10.15953/j.ctta.2022.065. (in Chinese)

基于3MNet去噪网络的快速EPRI成像

doi: 10.15953/j.ctta.2022.065
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

Fast EPRI Imaging Based on 3MNet Denoising Network

  • 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。

     

  • 图  1  CNN去噪示意图

    Figure  1.  CNN denoising schematic diagram

    图  2  3MNet网络结构图

    Figure  2.  3MNet network structure diagram

    图  3  双重注意力模块

    Figure  3.  Dual attention module

    图  4  残差密集连接块

    Figure  4.  Residual dense connecting blocks

    图  5  空洞卷积模块

    Figure  5.  Dilated convolution module

    图  6  通道注意力模块

    Figure  6.  Channel attention module

    图  7  多尺度特征融合模块

    Figure  7.  Multi-scale feature fusion module

    图  8  EPRI氧成像仪示意图

    Figure  8.  Schematic of the EPRI oxygen imager

    图  9  EPRI小瓶子示意图

    Figure  9.  Schematic of EPRI small bottles

    图  10  EPRI图像示意图

    Figure  10.  Schematic of EPRI images

    图  11  去噪结果示意图

    Figure  11.  Diagram of denoising results

    图  12  去噪结果局部放大图

    Figure  12.  Local enlarged view of denoising results

    图  13  3MNet内部子网络实验对比图

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

    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

    表  1  去噪实验对比结果

    Table  1.   Comparison results of denoising experiments

    方法  X Y Z
    R/dBeR/dBeR/dBe
       DnCNN82.1421.563e-4 85.1751.102e-4 82.2341.547e-4
       RED-CNN83.4951.338e-488.4287.580e-585.4401.070e-4
       UNet89.1576.970e-589.2576.890e-589.0927.020e-5
       CBDNet92.8374.562e-595.4983.358e-595.2353.462e-5
       BRDNet93.5194.218e-596.9012.858e-596.0353.157e-5
       PRIDNet93.9434.017e-596.2843.068e-595.7703.255e-5
       3MNet96.7662.902e-597.2572.743e-596.5142.988e-5
    下载: 导出CSV

    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/dBe(×10-5
        Remove both93.3634.294
        Remove image preprocessing95.3223.427
        Remove multiscale convolution95.6473.301
        3MNet96.7662.902
    下载: 导出CSV
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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

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