School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211000, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.
Med Biol Eng Comput. 2019 Oct;57(10):2145-2158. doi: 10.1007/s11517-019-02017-7. Epub 2019 Aug 3.
To reduce the magnetic resonance imaging (MRI) data acquisition time and improve the MR image reconstruction performance, reconstruction algorithms based on the iterative shrinkage thresholding algorithm (ISTA) are widely used. However, these traditional algorithms use global threshold shrinkage, which is not efficient. In this paper, a novel algorithm based on local threshold shrinkage, which is called the local shrinkage thresholding algorithm (LSTA), was proposed. The LSTA can shrink differently for different elements from the residual matrix to adjust the shrinkage speed for each element of the image during the iterative process. Then, by taking advantage of the sparser characteristics of the curvelet transform, the LSTA combined with the curvelet transform (CLSTA) can make the construction process more efficient. Finally, compared with ISTA, the generalized thresholding iterative algorithm (GTIA) and the fast iterative shrinkage threshold algorithm (FISTA), when analysing human (brain and cervical) MR images, a conclusion can be drawn that the proposed method has better reconstruction performance in terms of the mean square error (MSE), the peak signal to noise ratio (PNSR), the structural similarity index measure (SSIM), the normalized mutual information (NMI), the transferred edge information (TEI) and the number of iterations. The proposed method can better maintain the detailed information of the reconstructed images and effectively decrease the blurring of the images edges. Graphical abstract.
为了减少磁共振成像（MRI）数据采集时间并提高磁共振图像重建性能，基于迭代收缩阈值算法（ISTA）的重建算法被广泛应用。然而，这些传统算法使用全局阈值收缩，效率不高。本文提出了一种新的基于局部阈值收缩的算法，称为局部收缩阈值算法（LSTA）。LSTA 可以从残差矩阵中对不同的元素进行不同的收缩，以在迭代过程中调整图像每个元素的收缩速度。然后，利用曲波变换的稀疏特性，将 LSTA 与曲波变换（CLSTA）相结合，可以使构建过程更高效。最后，与 ISTA、广义阈值迭代算法（GTIA）和快速迭代收缩阈值算法（FISTA）相比，在分析人脑（脑和颈椎）MR 图像时，得出结论，所提出的方法在均方误差（MSE）、峰值信噪比（PNSR）、结构相似性指数测量（SSIM）、归一化互信息（NMI）、转移边缘信息（TEI）和迭代次数方面具有更好的重建性能。所提出的方法可以更好地保持重建图像的细节信息，并有效减少图像边缘的模糊。
