• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Structure-adaptive sparse denoising for diffusion-tensor MRI
【24h】

Structure-adaptive sparse denoising for diffusion-tensor MRI

机译：扩散张量MRI的结构自适应稀疏去噪

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of classical magnetic resonance signals. In this paper, we propose a new denoising method for DT-MRI, called structure-adaptive sparse denoising (SASD), which exploits self-similarity in DWIs. We define a similarity measure based on the local mean and on a modified structure-similarity index to find sets of similar patches that are arranged into three-dimensional arrays, and we propose a simple and efficient structure-adaptive window pursuit method to achieve sparse representation of these arrays. The noise component of the resulting structure-adaptive arrays is attenuated by Wiener shrinkage in a transform domain defined by two-dimensional principal component decomposition and Haar transformation. Experiments on both synthetic and real cardiac DT-MRI data show that the proposed SASD algorithm outperforms state-of-the-art methods for denoising images with structural redundancy. Moreover, SASD achieves a good trade-off between image contrast and image smoothness, and our experiments on synthetic data demonstrate that it produces more accurate tensor fields from which biologically relevant metrics can then be computed.
机译：扩散张量磁共振成像（DT-MRI）正成为一种潜在的临床成像技术，因为它具有体内和非侵入性表征组织的潜力。但是，扩散加权图像（DWI）的获取通常会受到噪声和伪影的破坏，并且扩散加权信号的强度比传统的磁共振信号要弱。在本文中，我们提出了一种新的DT-MRI去噪方法，称为结构自适应稀疏去噪（SASD），它利用了DWI中的自相似性。我们基于局部均值和修改后的结构相似性指数定义相似性度量，以找到排列成三维阵列的相似面片集，并提出一种简单有效的结构自适应窗口追踪方法来实现稀疏表示这些数组。在二维主成分分解和Haar变换定义的变换域中，通过维纳收缩缩小所得结构自适应阵列的噪声分量。在合成和实际心脏DT-MRI数据上进行的实验表明，所提出的SASD算法优于具有结构冗余的去噪图像的最新方法。此外，SASD在图像对比度和图像平滑度之间取得了良好的折衷，我们对合成数据的实验表明，它可以产生更准确的张量场，然后可以从中计算出生物学相关的指标。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号