• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of biomedical optics >Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography
【24h】

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

机译：基于反向传播神经网络的扩散光学层析成像重建算法

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

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.
机译：漫射光学层析成像（DOT）是一种有前途的无创成像方式，能够通过量化光学参数来提供生物组织的功能特征。由于生物组织中光传播的高度扩散性和有限的边界测量，DOT图像重建是不适当的和不适的。用于DOT图像重建的广泛使用的正则化技术是Tikhonov正则化，它倾向于产生包含严重伪影的过度平滑和低质量的图像。必须为Tikhonov正则化准确选择一个正则化参数。为了克服这些局限性，我们开发了一种非迭代的重建方法，该方法可基于反向传播神经网络（BPNN）恢复光学特性。我们基于一组训练数据训练DOT图像重建之前的BPNN参数。 DOT图像重建是通过对受训网络进行一次评估来实现的。为了证明所提出算法的性能，我们将其与传统的基于Tikhonov正则化的重建方法进行了比较。实验结果表明，所提出的算法显着提高了图像质量和重建光学性能的定量精度。

著录项

  • 来源
    《Journal of biomedical optics》 |2019年第5期|051407.1-051407.12|共12页
  • 作者

    Jinchao Feng; Qiuwan Sun; Zhe Li; Zhonghua Sun; Kebin Jia;

  • 作者单位

    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China,Beijing Laboratory of Advanced Information Networks, Beijing, China;

    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China;

    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China,Beijing Laboratory of Advanced Information Networks, Beijing, China;

    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China,Beijing Laboratory of Advanced Information Networks, Beijing, China;

    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China,Beijing Laboratory of Advanced Information Networks, Beijing, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    diffuse optical tomography; back-propagation neural network; image reconstruction; inverse problem;

    机译：漫射光学层析成像反向传播神经网络影像重建;反问题;

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