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Structural and Functional Sensing of Bio-Tissues Based on Compressive Sensing Spectral Domain Optical Coherence Tomography

机译:基于压缩谱域光学相干断层扫描的生物组织的结构和功能感测

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摘要

In this paper, a full depth 2D CS-SDOCT approach is proposed, which combines two-dimensional (2D) compressive sensing spectral-domain optical coherence tomography (CS-SDOCT) and dispersion encoding (ED) technologies, and its applications in structural imaging and functional sensing of bio-tissues are studied. Specifically, by introducing a large dispersion mismatch between the reference arm and sample arm in SD-OCT system, the reconstruction of the under-sampled A-scan data and the removal of the conjugated images can be achieved simultaneously by only two iterations. The under-sampled B-scan data is then reconstructed using the classic CS reconstruction algorithm. For a 5 mm × 3.2 mm fish-eye image, the conjugated image was reduced by 31.4 dB using 50% × 50% sampled data (250 depth scans and 480 spectral sampling points per depth scan), and all A-scan data was reconstructed in only 1.2 s. In addition, we analyze the application performance of the CS-SDOCT in functional sensing of locally homogeneous tissue. Simulation and experimental results show that this method can correctly reconstruct the extinction coefficient spectrum under reasonable iteration times. When 8 iterations were used to reconstruct the A-scan data in the imaging experiment of fisheye, the extinction coefficient spectrum calculated using 50% × 50% data was approximately consistent with that obtained with 100% data.
机译:在本文中,一个全深度2D CS-SDOCT方法,提出了一种结合了二维(2D)压缩感测谱域光学相干断层扫描(CS-SDOCT)和色散编码(ED)的技术,并且其在结构的成像应用和生物组织的功能性检测进行了研究。具体而言,通过在SD-OCT系统中引入参考臂和样本臂之间的大的分散体不匹配时,欠采样的A扫描数据的重建和去除共轭图像可以同时仅通过两次迭代来实现。然后,将下采样的B扫描数据使用经典CS重建算法重建。对于一个5毫米×3.2毫米鱼眼图像中,共轭图像是使用50%×50%的采样数据(250次深度扫描和每深度扫描480个的光谱采样点)降低31.4分贝,所有的A扫描数据被重建在只有1.2秒。此外,我们分析了CS-SDOCT的局部均匀的组织功能检测的应用程序的性能。仿真和实验结果表明,该方法可以正确地重建在合理的迭代次数的消光系数谱。当8次迭代被用来重建在鱼眼的成像实验A扫描数据,消光系数谱,使用50%×50个%数据与,与100%的数据而获得的近似一致的计算。

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