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A MRI Denoising Method Based on 3D Nonlocal Means and Multidimensional PCA

机译:基于3D非局部均值和多维PCA的MRI降噪方法

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摘要

Recently nonlocal means (NLM) and its variants have been applied in the various scientific fields extensively due to its simplicity and desirable property to conserve the neighborhood information. The two-stage MRI denoising algorithm proposed in this paper is based on 3D optimized blockwise version of NLM and multidimensional PCA (MPCA). The proposed algorithm takes full use of the block representation advantageous of NLM3D to restore the noisy slice from different neighboring slices and employs MPCA as a postprocessing step to remove noise further while preserving the structural information of 3D MRI. The experiments have demonstrated that the proposed method has achieved better visual results and evaluation criteria than 3D-ADF, NLM3D, and OMNLM_LAPCA.
机译:最近,由于非本地均值(NLM)的简单性和保存邻居信息的理想特性,非本地均值(NLM)及其变体已广泛应用于各种科学领域。本文提出的两阶段MRI去噪算法基于NLM和多维PCA(MPCA)的3D优化分块版本。所提出的算法充分利用了NLM3D的块表示来从不同的相邻切片中恢复噪点切片,并采用MPCA作为后处理步骤以进一步去除噪声,同时保留3D MRI的结构信息。实验表明,与3D-ADF,NLM3D和OMNLM_LAPCA相比,该方法具有更好的视觉效果和评估标准。

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