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Denoising Multi-coil Magnetic Resonance Imaging Using Nonlocal Means on Extended LMMSE

机译:在延长LMMSE上使用非局部手段去噪多卷磁共振成像

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Denoising plays key role in the field of medical images. Reliable estimation and noise removal is very important for accurate diagnosis of the disease. This should be done in such a way that original resolution is retained while maintaining the valuable features. Multi-coil Magnetic Resonance Image(MRI) trails nonsta-tionary noise following Rician and Noncentral Chi(nc-χ) distribution. On using the modern techniques which make use of multi-coil MRI like in GRAPPA would yield nc-χ distributed data. There has been lots of research done on the Rician nature but only few for nc-χ distribution. The proposed method uses Nonlocal Mean(NLM) on extended Linear Minimum Mean Square Error(ELMMSE) for denoising multi-coil MRI having nc-χ distributed data. The performance of the nonlocal scheme on multi-coil MRI is evaluated based on PSNR, SSIM and MSE and the result indicates proposed scheme is better than the existing scheme including Non local Maximum Likelihood(NLML), adaptive NLML and ELMMSE.
机译:去噪在医学图像领域发挥着关键作用。可靠的估算和噪音去除对于精确诊断疾病非常重要。这应该以这样的方式完成,在保持有价值的功能的同时保留原始分辨率。在瑞典和非中心智(NC-χ)分布后,多线圈磁共振图像(MRI)落后于非稳定性噪声。使用现代技术在格拉帕中使用的多线圈MRI将产生NC-χ分布式数据。在瑞典性质上有很多研究,但只有NC-χ分布只有很少。所提出的方法在扩展线性最小均方误差(ELMMSE)上使用非局部均值(NLM),用于去噪具有NC-χ分布式数据的多线圈MRI。基于PSNR,SSIM和MSE评估非函数方案对多线圈MRI的性能,结果表明所提出的方案优于包括非本地最大似然(NLML),Adaptive NLML和ELMMSE的现有方案。

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