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. 2022 Oct 31:13:1040777.
doi: 10.3389/fphys.2022.1040777. eCollection 2022.

Cell image reconstruction using digital holography with an improved GS algorithm

Yuhao Jiang  1   2 , Hongzhong Li  3 , Yu Pang  1 , Jiwei Ling  1 , Hao Wang  1 , Yuling Yang  1 , Xinyu Li  1 , Yin Tian  1   4 , Xiuxin Wang  1   5
Affiliations

Cell image reconstruction using digital holography with an improved GS algorithm

Yuhao Jiang et al. Front Physiol. .

Abstract

Digital holography is an effective technology in image reconstruction as amplitude and phase information of cells can be acquired without any staining. In this paper, we propose a holographic technique with an improved Gerchberg-Saxton (GS) algorithm to reconstruct cell imaging based on phase reconstruction information. Comparative experiments are conducted on four specific models to investigate the effectiveness of the proposed method. The morphological parameters (such as shape, volume, and sphericity) of abnormal erythrocytes can be obtained by reconstructing cell hologram of urinary sediment. Notably, abnormal red blood cells can also be detected in mussy circumstances by the proposed method, owing to the significantly biophysical contrast (refractive index distribution and mass density) between two different cells. Therefore, this proposed method has a broad application prospect in cell image reconstruction and cell dynamic detection.

Keywords: GS algorithm; cell image; computer hologram; phase reconstruction; reconstruction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
General setup of the holographic imaging measurement system.
FIGURE 2
FIGURE 2
Schematic of the improved algorithm calculating steps.
FIGURE 3
FIGURE 3
Original urine sediment image.
FIGURE 4
FIGURE 4
Pretreatment of image of urine sediment.
FIGURE 5
FIGURE 5
Amplitude reconstruction-based holographic image for Model 1. (A) Original greyscale image, (B) 20 iterative reconstruction images of amplitude information, (C) 200 iterative reconstruction images of amplitude information, and (D) 2000 iterative reconstruction images of amplitude information.
FIGURE 6
FIGURE 6
Phase reconstruction-based holographic image for Model 2 using the conventional GS algorithm. (A) Initial random phase, (B) 20 iterative reconstruction images of phase information, (C) 200 iterative reconstruction images of phase information, and (D) 2000 iterative reconstruction images of phase information.
FIGURE 7
FIGURE 7
Phase reconstruction-based holographic image for Model 3 using the improved GS algorithm with the optimal initial phase. (A) Optimized initial phase. (B) Twenty iterative reconstruction images of phase information. (C) 200 iterative reconstruction images of phase information. (D) 2000 iterative reconstruction images of phase information.
FIGURE 8
FIGURE 8
Phase reconstruction-based holographic image for Model 4 using the improved GS algorithm. (A) Optimized initial phase, (B) 20 iterative reconstruction images of phase information, (C) 200 iterative reconstruction images of phase information, and (D) 2000 iterative reconstruction images of phase information.
FIGURE 9
FIGURE 9
Phase energy map using the improved GS algorithm for different models. (A) Phase energy map with 20 iterations for Model 2. (B) Phase energy map with 200 iterations for Model 2. (C) Phase energy map with 2,000 iterations for Model 2. (D) Phase energy map with 20 iterations for Model 3. (E) Phase energy map with 200 iterations for Model 3. (F) Phase energy map with 2,000 iterations for Model 3. (G) Phase energy map with 20 iterations for Model 4. (H) Phase energy map with 200 iterations for Model 4. (I) Phase energy map with 2,000 iterations for Model 4.
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