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首页> 外文期刊>WSEAS Transactions on Signal Processing >A Novel Compressive Sampling MRI Method Using Variable-Density k-Space Under-sampling and Substitution of Coefficients
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A Novel Compressive Sampling MRI Method Using Variable-Density k-Space Under-sampling and Substitution of Coefficients

机译：一种新的压缩采样MRI方法，使用可变密度k空间取样和系数替代

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摘要

A fast Magnetic Resonance Imaging (MRI) algorithm that also reduces reconstruction artifacts is proposed in this paper. The method employs a variable-density k-space under-sampling scheme that reduces the image acquisition time. The under-sampled k-space is converted to an MR image that is corrupted by artifacts. The image is fully sampled using a sub-Gaussian random sampling matrix prior to being reconstructed in the Discrete Wavelet Transform (DWT) domain using a Compressive Sampling (CS) greedy method. The k-space coefficients that are acquired during the under-sampling step are used to replace their corresponding coefficients in the k-space of the compressively reconstructed image. Computer simulation test results are used to compare the performance of the proposed algorithm to other reported CS methods based on the Peak-Signal-to-Noise Ratio (PSNR) and the Structured SIMilarity (SSIM) measures. The results show that the proposed method yields an average PSNR improvement of 1.76 dB compared to the Orthogonal Matching Pursuit method (OMP). This translates to a 13% reduction in scan time for a given quality of the reconstructed image.
机译：本文提出了一种快速磁共振成像（MRI）算法还减少了重建伪像。该方法采用可变密度k空间下采样方案，其减少了图像采集时间。被拒绝的k空间被转换为由工件损坏的MR图像。使用压缩采样（CS）贪婪方法在离散小波变换（DWT）域中重建之前，使用子高斯随机采样矩阵完全采样图像。在下采样步骤期间获取的k空间系数用于替换压缩重建图像的k空间中的相应系数。计算机模拟测试结果用于将所提出的算法的性能与基于峰值信噪比（PSNR）和结构相似度（SSIM）测量的其他报告的CS方法进行比较。结果表明，与正交匹配追踪方法（OMP）相比，该方法的平均PSNR改善为1.76dB。这转化为扫描时间的13％，用于重建图像的给定质量的扫描时间。

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