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基于自适应稀疏表达的并行磁共振重建方法

Parallel Magnetic Resonance Imaging Reconstruction via Adaptive Sparse Representation

  • 摘要: 为精确地进行并行磁共振成像, 文章利用字典学习的强大捕捉细节和稀疏开发能力, 提出了一种基于自适应稀疏表达的重建方法。该方法将并行磁共振重建问题转化为最小化由字典学习和数据拟合项构成的目标函数, 并采用了分而治之的方案求解未知变量。为验证其有效性, 将该方法与目前主流的两种方法在人体实际磁共振数据上进行了测试。测试结果显示, 文章提出的方法能在抑制图像噪声的同时较好地保存图像细节。

     

    Abstract: An adaptive sparse representation regularized reconstruction method for accurate parallel imaging was proposed by exploring the strength of dictionary learning in capturing image fine structures while promoting sparsity. The reconstruction was formulated as a minimization problem, which consisted of a data-fidelity term and a dictionary learning term and was solved by the “divide and conquer” strategy. The comparative results of the proposed method with respect to two popular approaches on an in-vivo dataset demonstrated that the proposed method preserves more image fine details while suppressing noise.

     

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