...
首页> 外文期刊>Applied optics >Comparison of image reconstruction techniques for optical projection tomography
【24h】

Comparison of image reconstruction techniques for optical projection tomography

机译:光投影断层扫描图像重构技术的比较

获取原文
获取原文并翻译 | 示例
           

摘要

We present a comparison of image reconstruction techniques for optical projection tomography. We compare conventional filtered back projection, sinogram filtering using the frequency-distance relationship (FDR), image deconvolution, and 2D point-spread-function-based iterative reconstruction. The latter three methods aim to remove the spatial blurring in the reconstructed image originating from the limited depth of field caused by the point spread function of the imaging system. The methods are compared based on simulated data, experimental optical projection tomography data of single fluorescent beads, and high-resolution optical projection tomography imaging of an entire zebrafish larva. We demonstrate that the FDR method performs poorly on data acquired with high numerical aperture optical imaging systems. We show that the deconvolution technique performs best on highly sparse data with low signal-to-noise ratio. The point-spread-function-based reconstruction method is superior for nonsparse objects and data of high signal-to-noise ratio. (C) 2018 Optical Society of America
机译:我们介绍了光投影层析成像的图像重建技术的比较。我们使用频率 - 距离关系(FDR),图像解卷积和基于2D点扩展功能的迭代重建进行比较传统的过滤的后投影,铭记滤波。后三种方法旨在在由成像系统的点扩散函数引起的源自引起的有限景深的重建图像中去除空间模糊。这些方法基于模拟数据,单荧光珠的实验光投影断层扫描数据,以及整个斑马鱼幼虫的高分辨率光学投影层析成像。我们展示了FDR方法对使用高数孔径光学成像系统获取的数据进行差。我们表明Deconvolution技术在具有低信噪比的高度稀疏数据上表现最佳。基于点扩展功能的重建方法优于非分散对象和高信噪比的数据。 (c)2018年光学学会

著录项

  • 来源
    《Applied optics》 |2018年第8期|共9页
  • 作者单位

    Delft Univ Technol Dept Imaging Phys Lorentzweg 1 NL-2628 CJ Delft Netherlands;

    Delft Univ Technol Dept Imaging Phys Lorentzweg 1 NL-2628 CJ Delft Netherlands;

    Delft Univ Technol Dept Imaging Phys Lorentzweg 1 NL-2628 CJ Delft Netherlands;

    Delft Univ Technol Dept Imaging Phys Lorentzweg 1 NL-2628 CJ Delft Netherlands;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 应用;
  • 关键词

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