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首页> 外文期刊>International journal of imaging systems and technology >A Modified POCS-Based Reconstruction Method for Compressively Sampled MR Imaging
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A Modified POCS-Based Reconstruction Method for Compressively Sampled MR Imaging

机译：一种改进的基于POCS的压缩采样MR成像重建方法

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摘要

One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled /(-space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft-thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Mary's Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal-to-noise ratio, artifact power, and correlation as compared to the reconstruction through low-resolution and POCS algorithms.
机译：压缩传感在磁共振成像中的应用中的一项艰巨任务是重建算法，该算法可以如实地从随机欠采样的/（-space）数据中恢复MR图像。基于迭代收缩的非线性恢复算法始于单个初始猜测和提出了一种基于软阈值从部分傅里叶数据中恢复原始MR图像的方法，该方法提出了一种基于凸集投影（POCS）算法的新方法，该方法先获取两张图像，然后在每次迭代时随机组合以估计原始MR。实验结果表明，该方法的性能得到了伦敦圣玛丽医院MRI扫描仪采集的原始数据的验证，实验结果表明，该方法能够以更少的图像数量从可变密度欠采样方案重建MR图像。迭代，并在改善的信噪比，伪像功率和cor方面表现出更好的性能与通过低分辨率和POCS算法进行的重建相比，这种关系。

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