基于压缩感知的频域OCT图像稀疏重构

陈明惠; 王帆; 张晨曦; 李福刚; 郑刚

doi:10.3788/OPE.20202801.0189

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基于压缩感知的频域OCT图像稀疏重构

信息科学 | 更新时间：2020-08-13

- 基于压缩感知的频域OCT图像稀疏重构
- Sparse reconstruction of frequency domain OCT image based on compressed sensing
- 光学精密工程 2020年28卷第1期页码：189-199
- 作者机构：
  
  1.上海理工大学生物医学光学与视光学研究所教育部现代微创医疗器械及技术工程研究中心上海介入医疗器械工程技术研究中心，上海 200093
  2.上海奥普生物医药有限公司，上海 201203
- 作者简介：
  
  [ "陈明惠(1981-)，女，福建南靖人，博士，副教授，硕士生导师，2012年于浙江大学获得博士学位，主要从事生物医学光子学方面的研究。E-mail:cmhui.43@163.com" ]
- 基金信息：
  
  国家自然科学基金青年科学基金资助(6130115);上海市自然科学基金资助项目(13ZR1457900);上海市科委产学研医项目(15DZ1940400)
- DOI：10.3788/OPE.20202801.0189
  中图分类号： TP394.1
- 收稿日期：2019-07-30，
  
  录用日期：2019-9-4，
  
  纸质出版日期：2020-01-15
- 稿件说明：
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陈明惠, 王帆, 张晨曦, 等. 基于压缩感知的频域OCT图像稀疏重构[J]. 光学精密工程, 2020,28(1):189-199.

Ming-hui CHEN, Fan WANG, Chen-xi ZHANG, et al. Sparse reconstruction of frequency domain OCT image based on compressed sensing[J]. Optics and precision engineering, 2020, 28(1): 189-199.
陈明惠, 王帆, 张晨曦, 等. 基于压缩感知的频域OCT图像稀疏重构[J]. 光学精密工程, 2020,28(1):189-199. DOI： 10.3788/OPE.20202801.0189.

Ming-hui CHEN, Fan WANG, Chen-xi ZHANG, et al. Sparse reconstruction of frequency domain OCT image based on compressed sensing[J]. Optics and precision engineering, 2020, 28(1): 189-199. DOI： 10.3788/OPE.20202801.0189.

摘要

为了减轻频域光学相干断层扫描成像(Frequency Domain Optical Coherence Tomography，FD-OCT)中高数据量导致的后续数据采集与处理系统的压力，同时解决成像时间和成像质量之间的矛盾，引入了压缩感知技术，并对该技术中的重构算法进行了重点研究。首先，通过分析压缩感知技术的框架，利用离散余弦变换(Discrete Cosine Transform，DCT)获得频域OCT图像的稀疏表示。接着，利用高斯随机矩阵对OCT图像进行线性观测。然后，研究了FOCUSS(Focal Underdetermined System Solver)重构算法的原理，并在算法中结合分块思想、引入正则项

范数以及嵌入各向异性平滑算子。最后，组合所有小图像块，得到整幅频域OCT图像的压缩感知重构结果。实验结果表明：改进重构算法的运行时间由78.65 s缩短为1.89 s，并且显著改善了图像块效应，将重构图像的PSNR值提高了1.6~2.7 dB，SSIM值可达到0.938 3。压缩感知技术可以用较小的采样数据量精确重构出原始频域OCT图像，改进FOCUSS重构算法可以在一定程度上实现频域OCT图像重构效率和重构质量的平衡。

Abstract

In order to alleviate the pressure of subsequent data acquisition and processing systems caused by high data volume in Frequency Domain Optical Coherence Tomography (FD-OCT)

and to address the contradiction between imaging time and imaging quality

we introduced compressed sensing technology and focus on the reconstruction algorithm in this technology. First

we analyzed the framework of the compressed sensing technology

the frequency domain OCT image was sparsely represented by Discrete Cosine Transform. Next

we used Gaussian random matrices to perform linear observations on OCT images. Then

we studied the principle of FOCUSS (Focal Underdetermined System Solver) reconstruction algorithm

and combined the block idea

introduced the regular term

norm and embed anisotropic smoothing operator in the algorithm. Finally

we combined all the small image blocks to obtain the compressed sensing reconstruction result of the whole frequency domain OCT image. Experimental results indicate that the running time of the improved reconstruction algorithm is shortened from 78.65 s to 1.89 s

and the image block effect is significantly improved

the PSNR value of the reconstructed image is improved by 1.6-2.7 dB

and the SSIM value can reach 0.938 3. Compressed sensing technology can accurately reconstruct the original frequency domain OCT image with a small amount of sampled data. The improved FOCUSS reconstruction algorithm can achieve the balance of frequency domain OCT image reconstruction efficiency and reconstruction quality to some extent.

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