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A robust magnetic resonance imaging method based on compressive sampling and clustering of sparsifying coefficients

机译：一种基于压缩采样的鲁棒磁共振成像方法和稀疏系数的聚类

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This paper presents a novel and robust method for medical Magnetic Resonance Imaging (MRI). The proposed method utilizes the sparsity as well as clustering of the image coefficients in the wavelet transform sparsifying domain. The method shows better immunity to reconstruction noise than other Compressive Sampling (CS) based techniques. The algorithm starts with undersampling of the k-space data of the image using a random matrix followed by reconstruction of the Haar transform coefficients of the k-space data using the Orthogonal Matching Pursuit (OMP) algorithm. The transform coefficients are then modulated by a raised-cosine shaping vector that suppresses noisy artifacts in the coefficients to restore the clustering. The shaped coefficients are then transformed into k-space data. The k-space data is finally transformed into the image in spatial domain. Experimental results show that the proposed procedure gives better results than other conventional methods in terms of terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).
机译：本文介绍了一种用于医学磁共振成像（MRI）的新颖稳健方法。所提出的方法利用稀疏性以及小波变换稀疏域中的图像系数的聚类。该方法表明了基于其他压缩采样（CS）技术的重建噪声更好的抗扰度。该算法利用随机矩阵从图像的k空间数据的下采样开始，然后使用正交匹配追踪（OMP）算法重建K空间数据的HAAR变换系数。然后通过凸起的 - 余弦整形载体调制变换系数，该凸起 - 抑制系数中的噪声伪像来恢复聚类。然后将成形系数转换为k空间数据。最终将k空间数据转换为空间域中的图像。实验结果表明，在峰值信号与噪声比（PSNR）和均方误差（MSE）方面，该方法提供了比其他传统方法更好的结果。

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