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基于对抗式残差密集深度神经网络的CT稀疏重建

杜聪聪 乔志伟

杜聪聪, 乔志伟. 基于对抗式残差密集深度神经网络的CT稀疏重建[J]. CT理论与应用研究, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032
引用本文: 杜聪聪, 乔志伟. 基于对抗式残差密集深度神经网络的CT稀疏重建[J]. CT理论与应用研究, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032
DU C C, QIAO Z W. Sparse CT reconstruction based on adversarial residual dense deep neural network[J]. CT Theory and Applications, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032. (in Chinese)
Citation: DU C C, QIAO Z W. Sparse CT reconstruction based on adversarial residual dense deep neural network[J]. CT Theory and Applications, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032. (in Chinese)

基于对抗式残差密集深度神经网络的CT稀疏重建

doi: 10.15953/j.ctta.2021.032
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));山西省重点研发计划(电子顺磁共振成像(EPRI)中美联合实验室平台建设(201803D421012));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:

    杜聪聪:女,山西大学计算机科学与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:15535734519@163.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

  • 中图分类号: O  242;TP  391.41

Sparse CT Reconstruction Based on Adversarial Residual Dense Deep Neural Network

  • 摘要: 针对计算机断层成像稀疏重建过程中产生条状伪影的问题,本文提出一种基于对抗式残差密集深度神经网络的CT图像高精度稀疏重建方法。设计一种耦合残差连接、密集连接、注意力机制和对抗机制的UNet网络,以含条状伪影图像和高精度图像作为训练样本,通过大规模训练数据,对该网络进行训练,使其具有压制条状伪影的能力。首先,利用滤波反投影算法从稀疏投影中重建出含条状伪影的CT图像;接着,将其输入深度网络,通过网络压制条状伪影;最后,得到高精度的重建图像。实验结果表明,相比于现有的若干深度学习算法,提出的新型网络重建出的图像精度更高,可以更好地压制条状伪影。

     

  • 图  1  对抗式网络框架

    Figure  1.  Adversarial network framework

    图  2  残差密集生成网络结构图

    Figure  2.  Residual dense generative network structure diagram

    图  3  RAD块结构图

    Figure  3.  RAD block structure diagram

    图  4  注意力模块结构图

    Figure  4.  Attention module structure diagram

    图  5  判别网络结构图

    Figure  5.  Discriminative network structure diagram

    图  6  第一幅CT测试图结果

    Figure  6.  Results of the first CT test image

    图  7  第二幅CT测试图结果

    Figure  7.  Results of the second CT test image

    图  8  不同稀疏情形下的重建图像对比图

    Figure  8.  Comparison of reconstructed images under different sparsity

    表  1  稀疏重建结果的RMSE、SSIM和PSNR分析

    Table  1.   RMSE, SSIM and PSNR analysis of sparse-view reconstruction results

    重建方法

    FBPRED-CNNFBPConvNetFD-UNet本文方法
    PSNR22.230 28.800 34.440 35.180 35.240
    RMSE0.077 0.036 0.019 0.017 0.017
    SSIM0.645 0.923 0.967 0.969 0.970
    PSNR19.740 27.210 35.660 35.930 36.150
    RMSE0.103 0.044 0.016 0.016 0.016
    SSIM0.593 0.941 0.981 0.979 0.981
    下载: 导出CSV

    表  2  不同稀疏度下重建图像的PSNR值、SSIM值、RMSE值

    Table  2.   RMSE, SSIM and PSNR analysis of reconstruction results under different sparsity

    投影个数
    15306090
    PSNR29.77031.45034.69034.810
    RMSE0.0320.0270.0180.018
    SSIM0.9080.9300.9640.964
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-08
  • 录用日期:  2021-11-18
  • 网络出版日期:  2021-11-25
  • 刊出日期:  2022-04-01

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