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基于深度能量模型的低剂量CT重建

朱元正 吕启闻 官瑜 刘且根

朱元正, 吕启闻, 官瑜, 等. 基于深度能量模型的低剂量CT重建[J]. CT理论与应用研究, 2022, 31(0): 1-12. DOI: 10.15953/j.ctta.2021.077
引用本文: 朱元正, 吕启闻, 官瑜, 等. 基于深度能量模型的低剂量CT重建[J]. CT理论与应用研究, 2022, 31(0): 1-12. DOI: 10.15953/j.ctta.2021.077
ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction via deep energy based model[J]. CT Theory and Applications, 2022, 31(0): 1-12. DOI: 10.15953/j.ctta.2021.077. (in Chinese)
Citation: ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction via deep energy based model[J]. CT Theory and Applications, 2022, 31(0): 1-12. DOI: 10.15953/j.ctta.2021.077. (in Chinese)

基于深度能量模型的低剂量CT重建

doi: 10.15953/j.ctta.2021.077
基金项目: 国家优秀青年科学基金(先验信息表示与医学成像重建(62122033));国家自然科学基金面上项目(面向可解释性高维度卷积网络表示的快速磁共振成像研究(61871206));江西省2020年度研究生创新专项资金项目(基于流形生成模型的图像逆问题研究(YC2020-S107))。
详细信息
    作者简介:

    朱元正:男,南昌大学生物医学工程专业硕士研究生,主要从事基于深度学习CT成像算法的模型研究与应用,E-mail:zhuyuanzheng@email.ncu.edu.cn

    刘且根:男,南昌大学信息工程学院教授、博士生导师,电气及电子工程师学会高级成员,主要从事面向深度学习的医学成像算法的理论研究与应用,E-mail:liuqiegen@ncu.edu.cn

    通讯作者:

    男,南昌大学信息工程学院教授、博士生导师,电气及电子工程师学会高级成员,主要从事面向深度学习的医学成像算法的理论研究与应用,E-mail:liuqiegen@ncu.edu.cn

  • 中图分类号: O  242; TP  391

Low-dose CT Reconstruction Via Deep Energy Based Model

  • 摘要: 降低计算机断层扫描(CT)的剂量对于降低临床应用中的辐射风险至关重要,深度学习的快速发展和广泛应用为低剂量CT成像算法的发展带来了新的方向。与大多数受益于手动设计的先验函数或有监督学习方案的现有先验驱动算法不同,本文使用基于深度能量模型来学习正常剂量CT的先验知识,然后在迭代重建阶段,将数据一致性作为条件项集成到低剂量CT的迭代生成模型中,通过郎之万动力学迭代更新训练的先验,实现低剂量CT重建。实验比较,证明所提方法的降噪和细节保留能力优良。

     

  • 图  1  郎之万动力学采样迭代过程

    Figure  1.  Langevin dynamics sampling iterative process

    图  2  模型训练流程与网络结构

    Figure  2.  Model training process and network structure

    图  3  不同方法的CIRS挑战数据的重建结果

    Figure  3.  Reconstruction results of CIRS challenge data by different methods

    图  4  原始CT图像与不同算法重建的CT图像之间的残差图

    Figure  4.  The absolute difference images between the reference CT image and the CT images reconstructed by different algorithms

    图  5  在图3(a1)中红线一维数值强度分布(对比所有实验结果)

    Figure  5.  The one-dimensional numerical intensity distribution of the red line in Fig.3 (a1) (compare all experimental results)

    图  6  不同方法的AAPM数据集的重建结果

    Figure  6.  Reconstruction results of AAPM challenge data for different methods

    图  7  使用AAPM数据集的原始CT图像与不同算法重建结果之间的残差图

    Figure  7.  The absolute difference images between the original CT image and the CT images reconstructed from the different algorithms on AAPM dataset

    图  8  在图6(a1)中红线一维数值强度分布(对比所有实验结果)

    Figure  8.  The one-dimensional numerical intensity distribution of the red line in Fig.6 (a1) (compare all experimental results)

    表  1  EBM-LDCT算法的重建过程

    Table  1.   The reconstruction process of the EBM-LDCT algorithm

    算法1
    初始化:输入数据${\tilde x^0}\sim N(0,{\boldsymbol{I}})$,动量项${{\boldsymbol{w}}^0}$,参数$ \beta $,松弛因子$ \gamma $,迭代次数$ T $
    开始循环:$t = 1,{\text{ }}2,{\text{ }} \cdots ,{\text{ }}T$
      设定$ { {\boldsymbol{z} }^t}\sim N(0,{\boldsymbol{I} }) $
      根据公式(10)更新:$ {\tilde {\boldsymbol{u} }^t} = {\tilde {\boldsymbol{x} }^{t - 1} } - \displaystyle\dfrac{ {\,\varepsilon \,} }{2}{\nabla _x}E{}_\theta \left( { { {\tilde {\boldsymbol{x} } }^{t - 1} } } \right) + \sqrt \varepsilon { {\boldsymbol{z} }^t} $
      根据公式(8)更新:$ {\tilde {\boldsymbol{x} }^t} = { {\boldsymbol{w} }^{t - 1} } - \displaystyle\dfrac{ { {\kern 1 pt} { {\boldsymbol{A} }^{\rm{T} } }\left( { {\boldsymbol{A} }{ {\tilde {\boldsymbol{x} } }^{t - 1} } - {\boldsymbol{y} } } \right) + \beta \left( { { {\tilde {\boldsymbol{x} } }^{t - 1} } - { {\tilde {\boldsymbol{u} } }^t} } \right){\kern 1 pt} } }{ { { {\boldsymbol{A} }^{\rm{T} } }{\boldsymbol{A} }{\boldsymbol{1} } + \beta {\boldsymbol{1} } } } $
      最后更新:${{\boldsymbol{w}}^t} = {\tilde {\boldsymbol{x}}^t} + \gamma \left( { { {\tilde {\boldsymbol{x}}}^t} - { {\tilde {\boldsymbol{x}}}^{t - 1} } } \right)$
      ${\tilde {\boldsymbol{x}}^t} \leftarrow {{\boldsymbol{w}}^t}$
    结束
    下载: 导出CSV

    表  2  使用CIRS数据集的多种低剂量CT重建方法定量结果比较

    Table  2.   Comparison of quantitative results of multiple LDCT reconstruction methods using CIRS dataset

    方法MAEPSNR/dBSSIM/10-2
    FBP(Ramp-filter)9.48±0.6440.67±0.5994.36±0.87
    TV7.64±0.4142.12±0.3596.58±0.51
    K-SVD7.01±0.1942.86±0.3497.01±0.36
    RED-CNN6.74±0.0741.76±0.1297.47±0.16
    EBM-LDCT6.29±0.2342.73±0.2097.81±0.27
    下载: 导出CSV

    表  3  使用AAPM数据集的多种LDCT重建方法定量结果比较

    Table  3.   Comparison of quantitative results of multiple LDCT reconstruction methods using AAPM dataset

    方法MAEPSNR/dBSSIM/10-2
    FBP(Ramp-filter) 67.69±13.1628.68±2.0344.43±7.18
    TV29.65±4.6034.98±1.3286.10±3.48
    K-SVD21.00±1.0435.68±2.2681.98±2.41
    RED-CNN16.97±1.0239.37±1.8794.78±0.56
    EBM-LDCT18.75±1.3139.56±0.5494.16±0.68
    下载: 导出CSV
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  • 收稿日期:  2021-12-23
  • 修回日期:  2022-02-14
  • 录用日期:  2022-02-16
  • 网络出版日期:  2022-04-30

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