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
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
Fig. 3. Schematic description of the network structure. Here
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.
Fig. 6. Reconstruction of mouse RBC from experimental data using the network trained on synthetic data. The images to the left show the Rytov reconstruction, which is the input to the network. The images to the right show the results of the TomoNet.
Fig. 7. Validation of the experimental result using semisynthetic measurements. (a) Overall scheme of semisynthetic measurement generation using DDA. (b) Phase difference maps for two randomly selected angles and the average maps for all angles. The color bars are in radians. Calculation of projection errors in retrieved phase information from experimental and semisynthetic measurements.
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