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Digital holographic microscopy (DHM) can obtain biological parameters and morphological information of cells by reconstructing holograms, which is different from traditional optical microscopy. The DHM is a three-dimensional imaging technology which is effective, non-contact and non-destructive. With the developments of the image sensor and the computing technology, it has made significant progress in the field of living cells detection, especially for red blood cell. Compared with the technologies which are widely used in the field of cell imaging such as con-focal laser scanning microscopy, scanning near-field optical microscopy and optical coherence tomography, the DHM has the advantages including wide FOV and high-resolution to achieve higher imaging and quality. This paper introduces the principle of recording and reconstruction of digital holography, and then analyzes the performance of three reconstruction algorithms using the Fresnel method, the convolution method and the angular spectrum method. The Fresnel method is suitable for the sample size larger than the image sensor. Both the convolution method and the angular spectrum method have an optimal reconstruction distance. When the reconstruction distance is different from the optimal distance, the resolution of the reconstructed image will decrease, and the angular spectrum method is better than the convolution method in overall performance. The DHM system for RBC measurements mainly adopts the convolution algorithm or the angular spectrum algorithm to implement numerical reconstruction. The systems of the in-line DHM, the off-axis DHM and the optical tweezers combining with off-axis DHM are introduced. These techniques use algorithms including Rayleigh-Sommerfeld back-propagation, the sharpness quantification, the watershed segmentation, the numerical refocusing and the thermal fluctuation to determine the focal plane position and obtain the best reconstruction distance of the RBCs, and further detect the shape change of the RBCs and extract the information of high-resolution blood vessel shape and blood flow velocity. These techniques can even achieve the dynamic tracking and measure three-dimensional volume of RBCs in real-time which is helpful for pathological studies such as diabetes, cardiovascular disease and Parkinson's disease. With its unique non-contact and non-destructive characteristics, the DHM realizes real-time and quantitative detection that is difficult to achieve with traditional three-dimensional microscopic imaging technologies.
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Keywords:
- digital holography /
- microscope /
- cell imaging /
- digital refocusing
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图 1 数字全息原理示意图
Figure 1. Optical layout of digital holography.
图 2 数字全息显微重建原理图
Figure 2. Optical layout of digital holographic reconstruction.
图 4 RBC形变与重建的复振幅实部信息之间联系的结果分析 (a)不同记录距离z下最佳反向重建距离z', 绿线代表实际实验, 红线代表模拟实验, 插图代表模拟实验所用RBC; (b)绿线代表实际实验RBC的Re(U), 红线与紫线分别代表模拟实验中初始RBC与变形后RBC的Re(U); (c)所用RBC比(b)所用小约20%, 绿线与红线分别代表实际实验与模拟实验所得Re(U)结果; (b)与(c)所用黑线表示RBC的记录距离[52]
Figure 4. The relationship between the RBC deformation and the real part of reconstructed amplitude. (a) The reconstruction distance z' to the focus at different recording distances z of the simulated RBC (red) and the experiment using a real RBC (green). Inset shows the Cassini model of the RBC used in the simulations; (b) the experiment using a real RBC (green), the reconstructed Re(U) for the simulated unaltered RBC and a deformed RBC represented by the red and blue curves, respectively; (c) reconstructed data from a ~20% smaller RBC compared with the one used in (b). Red and green curves represent the Re(U) of simulation and experiment, respectively; Gray vertical line in (b) and (c) indicates position of the RBC[52].
图 6 RBC的全息图与重建图像 (a) CMOS像感器拍摄FEP微管内RBC所得全息图; (b), (c), (d)分别表示不同重建深度下RBC重建图像, 箭头表示聚焦的RBC[51]
Figure 6. The hologram and the reconstruction images of RBCs. (a) The RBC hologram obtained from CMOS; (b), (c), and (d) represent RBC reconstruction images at different reconstruction depths, respectively. Each focused RBC is shown by an arrow[51].
图 8 RBC的相位重建图像 (a)重建的口腔形状RBC相位图像; (b)重建的盘状RBC相位图像; (c)重建后经分水岭算法分割的口腔形状RBC相位图像; (d)重建后经分水岭算法分割的盘状RBC相位图像; (e)分割的单个RBC相位图像; (f)(g)(h)分别经标记分水岭算法进一步分割得到的单个RBC的A、B、C部分[54]
Figure 8. The reconstructed phase image for RBCs (a) The reconstructed phase image for RBCs having a stomatocyte shape; (b) the reconstructed RBCs phase image for RBCs having a discocyte shape; (c)the segmented phase image for RBCs having a stomatocyte shape; (d) the segmented phase image for RBCs having a discocyte shape; (e) the segmented phase image for single RBC(f), (g) and (h) represent the A, B and C parts by the marker-controlled watershed algorithm in RBC, respectively[54].
图 10 对单一RBC图像进行数字重聚焦与相应的RBC光学体积测量 (a)通过人工聚焦方法与数字重聚焦方法对单一RBC重建所得振幅图与相位图; (b)A—G的振幅方差分布; (c)RBC在人工聚焦方法所得光学体积(黑线)与数字重聚焦方法所得光学体积(蓝线), 光学体积OV表示为平均值 ± 标准差[53]
Figure 10. Digital refocusing of a single red blood cell image and corresponding optical volume measurements. (a) The amplitude and phase images by the manually-focused method and digitally-refocused method from a single RBC; (b) amplitude variance metric of holograms A-G; (c) computed OV of RBC from manually-focused phase images(black) and digitally-refocused phase images(blue). OV reported as mean ± standard deviation[53].
图 12 0−3 pN陷阱拉伸力变化下不同浓度(0−200 μmol/L)氧化应激下重建RBC的高度变化, 颜色深浅代表RBC高度的大小[72]
Figure 12. Height change of reconstructed RBCs under different concentrations of oxidative stress (0−200 μmol/L). Four images in each group are corresponding to trap force varying from 0−3 pN. Color bar represents different thickness[72].
图 13 RBC在不同浓度氧化应激下的性能 (a)不同浓度氧化应激下RBC最大高度H与陷阱拉伸力关系; (b)不同浓度氧化应激下RBC体积[72]
Figure 13. Performance of RBC under different oxidative stress. (a) The relationship between the maximum height H of RBC and the trap tensile force under different oxidative stress; (b) the volume of RBC under different oxidative stress[72].
坐标 测量精度/μm 均方根误差/μm $x$ ± 0.3 0.63 $y$ ± 0.3 0.52 $z$ ± 1.0 2.05 
DownLoad: CSV
表 2 两种不同形状RBC的A、B部分的三维体积
Table 2. The different shapes of RBC’s three-dimensional volume of A and B parts.
A部分 B部分 平均体积/μm3 均方根
误差平均体积/μm3 均方根
误差口腔状 7.6 7.2 41.5 14.7 盘状 14.6 8.0 32.7 7.3
DownLoad: CSV
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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