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Accepted: Mar. 4, 2020

Posted: Mar. 25, 2020

Published Online: Mar. 25, 2020

The Author Email: Lim Joowon (limjoowon@gmail.com), Ayoub Ahmed B. (ahmed.ayoub@epfl.ch), Psaltis Demetri (demetri.psaltis@epfl.ch)

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Joowon Lim, Ahmed B. Ayoub, Demetri Psaltis. Three-dimensional tomography of red blood cells using deep learning[J]. Advanced Photonics, 2020, 2(2): 026001

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Fig. 1. The missing cone problem and overall scheme of the main idea. (a) Demonstration of the missing cone problem for a single RBC. The left two columns show the Rytov reconstruction and the right two columns show the ground truth. The first row displays the scattering potential, which can be converted to RI distributions, and the second row displays the $k$-spaces corresponding to the first row. (b) Overall scheme of the network.

Fig. 2. Dataset generation. (a) RBC model parameters. (b) Synthetic measurements generation using the DDA. (c) Generation of synthetic measurements for two RBCs: one RBC lying in the $xy$ plane and the same RBC but randomly rotated. The pairs of the Rytov reconstructions and the ground truth RBCs are presented. The scale represents the normalized RI, which is calculated by dividing the RI values of a sample by the RI of the background. (d) Schematic description of the $z$-shift variant property of the Rytov measurement.

Fig. 3. Schematic description of the network structure. Here $c$ represents the number of channels written at each block. WN, weight normalization; LRLU, leaky RELU; and LN, layer normalization.

Fig. 4. Schematic for the experimental setup. M, mirror; L, lens; OBJ, objective lens; and BS, beamsplitter.

Fig. 5. Reconstruction results using two examples from the test datasets. (a) Results for an RBC without rotation and (b) results for another RBC with rotation. The scale represents the normalized RI, which is calculated by dividing the RI values of a sample with the RI of background.