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首页> 外文期刊>IEEE Transactions on Medical Imaging >Hybrid CS-DMRI: Periodic Time-Variant Subsampling and Omnidirectional Total Variation Based Reconstruction
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Hybrid CS-DMRI: Periodic Time-Variant Subsampling and Omnidirectional Total Variation Based Reconstruction

机译：混合CS-DMRI：周期性时变子采样和基于全向总变化的重构

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摘要

Compressive sensing (CS) has been used to accelerate dynamic magnetic resonance imaging (DMRI). Currently, the online CS-DMRI is faster, whereas the offline CS-DMRI provides higher accuracy for image reconstruction. To achieve good image reconstruction performance in terms of both speed and accuracy, we propose a hybrid CS-DMRI method using periodic time-variant subsampling for different frames. In each period, there is one reference frame that is sampled at a higher subsampling ratio. The two nearby reference frames with good reconstruction quality can be used to provide rough predictions of the other frames between them. To finely recover the current frame, one structural regularization in the optimization model for reconstruction is a 2-D omnidirectional total variation (OTV) for exploiting the sparsity of the difference between the predicted and estimated frames, and the other is a 3-D OTV as a regularization term for exploiting the bilateral spatio-temporal coherence between the forward reference frame, current frame, and backward reference frame. Compared with classical total variation, the proposed OTV fully utilizes the correlations of all the possible directions of the data. The formulated optimization model can be solved using iterative reweighted least squares with the pre-conditioned conjugate gradient method. Numerical experiments demonstrate that the proposed method has better reconstruction accuracy than all the existing methods and low computational complexity that is comparable to the existing online methods.
机译：压缩感测（CS）已用于加速动态磁共振成像（DMRI）。当前，在线CS-DMRI速度更快，而离线CS-DMRI为图像重建提供了更高的准确性。为了在速度和精度上都达到良好的图像重建性能，我们提出了一种针对不同帧使用周期性时变子采样的混合CS-DMRI方法。在每个周期中，有一个参考帧以较高的子采样率采样。具有良好重构质量的两个附近参考帧可用于提供它们之间其他帧的粗略预测。为了精确地恢复当前帧，用于重建的优化模型中的一种结构化规则化是利用预测帧和估计帧之间差异的稀疏性的二维全向总变化（OTV），另一种是3-D OTV作为正则化术语，用于开发前向参考帧，当前帧和后向参考帧之间的双边时空一致性。与经典的总变化量相比，提出的OTV充分利用了数据所有可能方向的相关性。可以使用预处理的共轭梯度方法，使用迭代的加权最小二乘法求解公式化的优化模型。数值实验表明，与现有的在线方法相比，该方法具有更高的重构精度和较低的计算复杂度。

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  • 来源
    《IEEE Transactions on Medical Imaging》 |2017年第10期|2148-2159|共12页
  • 作者

    Yipeng Liu; Shan Wu; Xiaolin Huang; Bing Chen; Ce Zhu;

  • 作者单位

    School of Electronic Engineering/Center for Robotics/Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China;

    School of Electronic Engineering/Center for Robotics/Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China;

    Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;

    School of Electronic Engineering/Center for Robotics/Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China;

    School of Electronic Engineering/Center for Robotics/Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image reconstruction; Optimization; Image restoration; TV; Correlation; Current measurement; Matrix converters;

    机译：图像重建;优化;图像恢复;电视;相关;电流测量;矩阵转换器;

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