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PF-FICOTA-SENSE:一种MRI快速重构方法

李建武 康杨 周金鹏

李建武, 康杨, 周金鹏. PF-FICOTA-SENSE:一种MRI快速重构方法. 自动化学报, 2020, 46(5): 897-908. doi: 10.16383/j.aas.c180706
引用本文: 李建武, 康杨, 周金鹏. PF-FICOTA-SENSE:一种MRI快速重构方法. 自动化学报, 2020, 46(5): 897-908. doi: 10.16383/j.aas.c180706
LI Jian-Wu, KANG Yang, ZHOU Jin-Peng. PF-FICOTA-SENSE: An MRI Fast Reconstruction Method. ACTA AUTOMATICA SINICA, 2020, 46(5): 897-908. doi: 10.16383/j.aas.c180706
Citation: LI Jian-Wu, KANG Yang, ZHOU Jin-Peng. PF-FICOTA-SENSE: An MRI Fast Reconstruction Method. ACTA AUTOMATICA SINICA, 2020, 46(5): 897-908. doi: 10.16383/j.aas.c180706

PF-FICOTA-SENSE:一种MRI快速重构方法

doi: 10.16383/j.aas.c180706
基金项目: 

国家自然科学基金 61271374

详细信息
    作者简介:

    康杨  北京理工大学计算机学院硕士研究生.主要研究方向为医学图像处理与机器智能. E-mail: 1045352075@qq.com

    周金鹏  北京理工大学计算机学院硕士研究生.主要研究方向为医学图像处理与机器智能.E-mail: 2120121177@bit.edu.cn

    通讯作者:

    李建武  博士, 北京理工大学计算机学院副教授.主要研究方向为计算机视觉, 图像处理, 超分辨率图像重建技术.本文通信作者. E-mail: ljw@bit.edu.cn

PF-FICOTA-SENSE: An MRI Fast Reconstruction Method

Funds: 

National Natural Science Foundation of China 61271374

More Information
    Author Bio:

    KANG Yang  Master student at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers medical image processing and machine intelligence

    ZHOU Jin-Peng  Master student at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers medical image processing and machine intelligence

    Corresponding author: LI Jian-Wu  Ph. D., associate professor at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers computer vision, image processing, and super-resolution image reconstruction. Corresponding author of this paper
  • 摘要: 如何实现快速磁共振成像(Magnetic resonance imaging, MRI)是MRI医学图像技术发展和应用的关键, 现有的快速MRI成像技术在成像速度及成像质量方面仍存在很大的提升空间.本文基于Contourlet变换, 对磁共振图像进行稀疏表示, 并结合传统PF-CS-SENSE框架, 提出一种基于Contourlet变换的组合MRI重构方法, 即PF-FICOTA-SENSE.考虑到组合MRI采样模式、低频数据的对称性以及Contourlet能更好地拟合曲线轮廓等因素, 进一步提出一种快速组合MRI方法, 该方法通过将低频部分重建由FICOTA重建替换为直接填零的傅里叶重建, 来实现快速重建.对比实验表明, 无论在MRI重构速度还是重构质量方面, 本文算法均能取得更好的性能.
    Recommended by Associate Editor SANG Nong
    1)  本文责任编委 桑农
  • 图  1  PFPI流程

    Fig.  1  PFPI process

    图  2  CS-SENSE流程

    Fig.  2  CS-SENSE process

    图  3  半傅里叶成像Homodyne算法流程

    Fig.  3  Semi-Fourier imaging homodyne algorithm process

    图  4  并行成像SENSE算法

    Fig.  4  Parallel imaging SENSE algorithm

    图  5  PF-CS-SENSE流程

    Fig.  5  PF-CS-SENSE process

    图  6  PF-FICOTA-SENSE流程

    Fig.  6  PF-FICOTA-SENSE process

    图  7  变密度随机采样

    Fig.  7  Variable density random sampling

    图  8  快速组合MRI变密度随机采样

    Fig.  8  Fast combined MRI variable density random sampling

    图  9  基于快速组合MRI框架的PF-FICOTA-SENSE

    Fig.  9  PF-FICOTA-SENSE based on fast combined MRI framework

    图  10  三种方法的实验结果

    Fig.  10  Experimental results from three different methods

    图  11  三种方法的快速组合MRI实验结果$ (R_{Total}=6)$

    Fig.  11  Rapid combined MRI results $ (R_{Total}=6)$ of three different methods

    表  1  SFLCT不同配置下的冗余度数据

    Table  1  SFLCT redundancy data in different configurations

    $d$ $\omega_{p, 0}$ $\omega_{s, 0}$ $\omega_{p, 1}$ $\omega_{s, 1}$ 冗余度
    1 $\frac{\pi}{3}$ $\frac{2\pi}{3}$ $\frac{\pi}{6}$ $\frac{\pi}{3}$ 2.33
    1.5 $\frac{5\pi}{14}$ $\frac{9\pi}{14}$ $\frac{19\pi}{72}$ $\frac{35\pi}{72}$ 1.60
    2 $\frac{4\pi}{21}$ $\frac{10\pi}{21}$ $\frac{4\pi}{21}$ $\frac{10\pi}{21}$ 1.33
    下载: 导出CSV

    表  2  三种方法的重建性能比较(ROI)

    Table  2  Reconstruction performance comparison (ROI) on three different methods

    PF-FICOTA-SENSE PF-DTFCSA-SENSE PF-CS-SENSE
    稀疏表示 SFLCT Complex DT Daubechies-4
    重建时间(s) $\sim$150 $\sim$120 $\sim$150
    $R_{Total} = 6$
    $R_{CS} \approx 1.8$
    CC 0.9825 0.9816 0.9779
    PSNR 75.1207 74.7429 74.1084
    NMSE 0.0033 0.0036 0.0041
    $R_{Total} = 10$
    $R_{CS} \approx 3.2$
    CC 0.9717 0.9724 0.9557
    PSNR 73.0758 73.0504 71.1287
    NMSE 0.0052 0.0053 0.0082
    下载: 导出CSV

    表  3  三种方法的快速组合MRI重建性能比较(ROI)

    Table  3  Reconstruction performance comparison (ROI) on fast combined MRI of three different methods

    PF-FICOTA-SENSE PF-DTFCSA-SENSE PF-CS-SENSE
    组合框架 原型 快速 原型 快速 原型 快速
    重建时间(s) $\sim$150 $\sim$75 $\sim$120 $\sim$60 $\sim$150 $\sim$75
    $R_{Total} = 6$
    $R_{CS} \approx 1.8$
    CC 0.9825 0.9824 0.9816 0.9815 0.9779 0.9779
    PSNR 75.1207 75.1079 74.7429 74.7293 74.1084 74.1021
    NMSE 0.0033 0.0033 0.0036 0.0036 0.0041 0.0041
    $R_{Total} = 10$
    $R_{CS} \approx 3.2$
    CC 0.9717 0.9717 0.9724 0.9723 0.9557 0.9557
    PSNR 73.0758 73.0670 73.0504 73.0384 71.1287 71.1277
    NMSE 0.0052 0.0052 0.0053 0.0053 0.0082 0.0082
    下载: 导出CSV

    表  4  三种方法的快速组合MRI峰值信噪比(PSNR)损失率(ROI)

    Table  4  Peak signal-to-noise ratio (PSNR) loss rate (ROI) on fast combined MRI of three different methods

    PSNR损失率($\times{10}^{-4}$) PF-FICOTA-SENSE PF-DTFCSA-SENSE PF-CS-SENSE
    $R_{Total} = 6$
    $R_{CS} \approx 1.8$
    1.7039 1.8199 0.8501
    $R_{Total} = 10$
    $R_{CS} \approx 3.2$
    1.2041 1.6427 0.1406
    下载: 导出CSV
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