Advanced Photonics, Volume. 4, Issue 6, 066001(2022)
Physics-informed neural networks for diffraction tomography On the Cover
Fig. 1. Schematic description of MaxwellNet, with U-Net architecture, and its application for tomographic reconstruction. The input is a refractive index distribution and the output is the envelope of the scattered field. The output is modulated by the fast-oscillating term
Fig. 2. Results of MaxwellNet and its comparison with COMSOL. (a) and (b) Two test cases from the digital phantom dataset and the prediction of the real and imaginary parts of the envelope of the scattered fields using MaxwellNet, COMSOL, and their difference. (c) Scattered field predictions from the network trained in (a) and (b) for the case of an experimentally measured RI of HCT-116 cancer cells and comparison with COMSOL. The difference between the two is no longer negligible. (d) Comparison between MaxwellNet and COMSOL after fine-tuning the former for a set of HCT-116 cells. MaxwellNet predictions reproduces much more accurate results after fine-tuning.
Fig. 3. Results of 3D MaxwellNet and its comparison with COMSOL. The RI distribution is shown in (a). The real part of the envelope of the scattered field calculated by 3D MaxwellNet is shown in (b), calculated by COMSOL in (c), and their difference in (d). The imaginary part of the envelope of the scattered field calculated by 3D MaxwellNet, COMSOL, and their difference are presented in (e)–(g), respectively.
Fig. 4. Tomographic reconstruction of RI using MaxwellNet. (a) The RI reconstruction was achieved by Rytov, MaxwellNet, and the ground truth. (b) 1D RI profile at
Fig. 5. Tomographic RI reconstruction of 3D sample using MaxwellNet. The RI reconstruction is achieved by Rytov, MaxwellNet, learning tomography, and the ground truth in different rows at the
Fig. 6. Tomographic RI reconstruction of a polystyrene microsphere immersed in water. The projections are measured with off-axis holography for different angles. The RI reconstructions achieved by Rytov, MaxwellNet, and learning tomography are presented at the
Fig. 7. Training and fine-tuning of MaxwellNet. (a) Training (blue) and validation (orange) loss of MaxwellNet for the digital cell phantoms dataset. (b) Fine-tuning of the pretrained MaxwellNet for a dataset of HCT-116 cells for 1000 epochs. (c) Examples of the HCT-116 dataset.
Fig. 8. Experimental setup for multiple illumination angle off-axis holography. HW: half-wave plate; P: polarizer; BS: beam splitter; L: lens; Obj: microscope objective; and M: mirror.
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Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 2022, 4(6): 066001
Category: Research Articles
Received: Jul. 9, 2022
Accepted: Oct. 27, 2022
Posted: Oct. 27, 2022
Published Online: Nov. 25, 2022
The Author Email: Saba Amirhossein (amirhossein.sabashirvan@epfl.ch)