Chen Shanshan, Qiu Bensheng, Zhao Feng, Li Chao, Du Hongwei
Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China.
School of Computer Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK.
Int J Biomed Imaging. 2017;2017:9604178. doi: 10.1155/2017/9604178. Epub 2017 Apr 9.
Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.
多年来，压缩感知（CS）已被应用于加速磁共振成像（MRI）。由于小波基缺乏平移不变性，基于离散小波变换的欠采样MRI重建可能会导致严重的伪影。在本文中，我们提出了一种基于CS的重建方案，该方案将复数双密度双树离散小波变换（CDDDT-DWT）与快速迭代收缩/软阈值算法（FISTA）相结合，以有效减少此类视觉伪影。CDDDT-DWT具有平移不变性、高分辨率和良好的方向选择性。此外，FISTA具有出色的收敛速度，且设计简单。与传统的基于CS的重建方法相比，实验结果表明，这种新方法实现了更高的峰值信噪比（PSNR）、更大的信噪比（SNR）、更好的结构相似性指数（SSIM）和更低的相对误差。
