Northeast Petroleum University, Daqing 163318, China.
Northeast Petroleum University of Qinhuangdao, Qinhuangdao 066004, China.
Comput Intell Neurosci. 2022 Jul 6;2022:9222958. doi: 10.1155/2022/9222958. eCollection 2022.
Due to the fact that Magnetic Resonance Imaging (MRI) is still a relatively slow imaging modality, its application for dynamic imaging is restricted. The total variation is introduced into the CS-based MRI reconstruction model, and three regularization conditions are adopted to ensure that a high-quality reconstructed image is produced. In this paper, a simple yet fast CS-based optimization model for noisy MRI Enhancement is proposed. The alternative direction multiplier method is chosen to optimize the model, and the -terms power series is applied in order to derive the LogDet function into the augmented Lagrange form. Following this, an approximation of the feature vector is achieved through the iterative process. The quality of the reconstructed image was much better than that of the CS-based MRI image reconstruction algorithm, as shown by experimental results under different noise conditions. The peak signal-to-noise ratio of the reconstructed image was able to be improved anywhere from 5 to 20 percent.
由于磁共振成像（MRI）仍然是一种相对较慢的成像方式，因此其在动态成像中的应用受到限制。总变差被引入到基于 CS 的 MRI 重建模型中，并采用三种正则化条件来确保产生高质量的重建图像。本文提出了一种简单而快速的基于 CS 的噪声增强 MRI 优化模型。选择交替方向乘子法来优化模型，并应用 -terms 幂级数将对数行列式函数转换为增广拉格朗日形式。然后，通过迭代过程得到特征向量的近似值。实验结果表明，在不同噪声条件下，重建图像的质量明显优于基于 CS 的 MRI 图像重建算法。重建图像的峰值信噪比可以提高 5%到 20%不等。
