基于curvelet变换快速迭代收缩阈值算法的压缩采样磁共振图像重建

Compression sampling magnetic resonance image reconstruction based on curvelet-FISTA

目的为提高MR图像的重建效果和降低重建图像边缘模糊,本文提出一种基于curvelet变换的MRI快速迭代收缩阈值算法(fast iterative shrinkage-thresholding algorithm,FISTA)。方法利用curvelet变换多尺度、各向奇异性、对图像边缘有更好的几何表达等特性,将curvelet稀疏变换和FISTA结合,并与传统基于小波变换的FISTA对相同MR图像作重建对比。重建图像的质量以峰值信噪比(peak signal to noise ratio,PSNR)、均方误差(mean square error,MSE)、结构相似性度(structural similarity degree,SSIM)来衡量。结果实验选用Lena图像和脑部MR图像,从重建图像细节、差值图像、评估参数三方面对算法重建效果进行比较分析,证明该curvelet-FISTA算法可有效恢复完全采样图像从核磁共振成像中的欠采样数据。结论与传统基于小波变换的FISTA相比,该方法可以更好地保持重建图像的细节信息,并有效地消除图像边缘的模糊现象,显示了较好的重建效果。

Objective In order to improve the reconstruction effect of MR images and reduce the noise of reconstructed images, this paper proposes an MRI fast iterative shrinkage thresholding algorithm （FISTA） based on eurvelet tiansform. Methods using the characteristics of euiwelet transform as multi-scale, anisotropy, better geometrical expression of image edges, we combine the euiwelet sparse tiansform with FISTA, and compare the same MR image with the traditional FISTA. The quality of the reconstructed image is measured by peak signal to noise ratio （ PSNR）, mean square error （ MSE）, and structural similarity degree （ SSIM）. Results Lena image and brain MR image are used in the experiment. The reconstruction efteet of the algorithm is compared and analyzed flom the three aspects as the details of reconstruction image, the difteienee image and the evaluation parameter. The euiwelet-FISTA algorithm can effectively recover the undersampled data of the completely sampled image from the magnetic resonance imaging. Conclusions Compared with the traditional FISTA based on the wavelet transform, this method can better maintain the detailed information of the reconstructed image and effectively eliminate blurring at the edges of the image and shows a good reconstruction e^lect.