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首页> 外文期刊>NeuroImage >Denoise diffusion-weighted images using higher-order singular value decomposition
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Denoise diffusion-weighted images using higher-order singular value decomposition

机译：使用高阶奇异值分解的去噪扩散加权图像

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摘要

Abstract Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.
机译：摘要噪声通常影响扩散加权（DW）磁共振成像（MRI）中定量分析的可靠性，尤其是高B值和/或高空间分辨率。高阶奇异值分解（Hosvd）最近被出现为简单，有效和自适应的变换，以利用多维数据内的稀疏性。特别地，当施加到T1-，T2-和质子密度加权的MRI数据时，基于贴剂的HosVD去噪已经证明了极好的性能。在这项研究中，我们的目标是使用Hosvd变换来研究去噪数据的可行性。凭借典型的DW数据中的低信噪比，由于从嘈杂的斑块中学到的Hosvd基地，基于补丁的Hosvd脱颖而出的均匀造成的条纹伪影。为了解决这个问题，我们提出了一种新颖的去噪方法。它首先介绍了基于Hosvd的基于Hosvd的去噪，作为预热阶段，以引导基于补丁的Hosvd去噪阶段。然后，从预流图像中的补丁组的Hosvd基座用于在原始DW数据中转换噪声补丁组。使用模拟和体内DW数据进行实验。结果表明，该方法与传统的基于贴片的Hosvd去噪方法相比，该方法显着减少了条纹伪影，并且在去噪质量和扩散参数估计方面优于两个最先进的去噪方法。

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  • 来源
    《NeuroImage》 |2017年第2017期|共18页
  • 作者

    Xinyuan Zhang; Jie Peng; Man Xu; Wei Yang; Zhe Zhang; Hua Guo; Wufan Chen; Qianjin Feng; Ed X. Wu; Yanqiu Feng;

  • 作者单位

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    Department of Biomedical Engineering Center for Biomedical Imaging Research Tsinghua University;

    Department of Biomedical Engineering Center for Biomedical Imaging Research Tsinghua University;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

    School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 诊断学;
  • 关键词

    Diffusion-weighted imaging (DWI); Magnetic resonance imaging (MRI); Diffusion tensor imaging (DTI); Denoising; Higher-order singular value decomposition (HOSVD);

    机译：扩散加权成像（DWI）;磁共振成像（MRI）;扩散张量成像（DTI）;去噪;高阶奇异值分解（Hosvd）;

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