He Wei, Zhao Linman
Department of Computer Science and Technology, Xinyang Normal University, Xinyang 464000, China.
Henan Key Laboratory of Analysis and Applications of Education Big Data, Xinyang Normal University, Xinyang 464000, China.
Int J Biomed Imaging. 2022 Apr 29;2022:7251674. doi: 10.1155/2022/7251674. eCollection 2022.
The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.
基于目标变量的数量，压缩感知磁共振成像（CS-MRI）方法大致可分为两类。一类致力于估计复值MRI图像。另一类则通过对复值MRI图像的幅度和相位部分分别施加单独的惩罚来计算它们。我们提出了一种基于双树复小波（DT CWT）稀疏性的新的基于CS的方法，该方法属于第二类CS-MRI框架。由于采用了单独的正则化框架，该方法减少了相位跳变（即相位值的跳变或不连续性）对幅度重建的影响。此外，凭借DT CWT的优异特性，如系数的非振荡包络和多方向选择性，该方法能够在幅度和相位图像中捕捉更多细节。实验结果表明，该方法能够很好地恢复图像轮廓和边缘信息，并能消除由相位跳变引起的幅度结果中的伪影。
