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| 1 | Three-dimensional tomography of red blood cells using deep learning显示文摘We accurately reconstruct three-dimensional(3-D)refractive index(RI)distributions from highly ill-posed two-dimensional(2-D)measurements using a deep neural network(DNN).Strong distortions are introduced on reconstructions obtained by the Wolf transform inversion method due to the ill-posed measurements acquired from the limited numerical apertures(NAs)of the optical system.Despite the recent success of DNNs in solving ill-posed inverse problems,the application to 3-D optical imaging is particularly challenging due to the lack of the ground truth.We overcome this limitation by generating digital phantoms that serve as samples for the discrete dipole approximation(DDA)to generate multiple 2-D projection maps for a limited range of illumination angles.The presented samples are red blood cells(RBCs),which are highly affected by the ill-posed problems due to their morphology.The trained network using synthetic measurements from the digital phantoms successfully eliminates the introduced distortions.Most importantly,we obtain high fidelity reconstructions from experimentally recorded projections of real RBC sample using the network that was trained on digitally generated RBC phantoms.Finally,we confirm the reconstruction accuracy using the DDA to calculate the 2-D projections of the 3-D reconstructions and compare them to the experimentally recorded projections. | Joowon Lim Ahmed B.Ayoub Demetri Psaltis | 2020 | Advanced Photonics2020，2，2： | 13 |
| 2 | Multimode optical fiber transmission with a deep learning network显示文摘Multimode fibers(MMFs)are an example of a highly scattering medium,which scramble the coherent light propagating within them to produce seemingly random patterns.Thus,for applications such as imaging and image projection through an MMF,careful measurements of the relationship between the inputs and outputs of the fiber are required.We show,as a proof of concept,that a deep neural network can learn the input-output relationship in a 0.75 m long MMF.Specifically,we demonstrate that a deep convolutional neural network(CNN)can learn the nonlinear relationships between the amplitude of the speckle pattern(phase information lost)obtained at the output of the fiber and the phase or the amplitude at the input of the fiber.Effectively,the network performs a nonlinear inversion task.We obtained image fidelities(correlations)as high as~98%for reconstruction and~94%for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix.We further show that the network can be trained for transfer learning,i.e.,it can transmit images through the MMF,which belongs to another class not used for training/testing. | Babak Rahmani Damien Loterie Georgia Konstantinou Demetri Psaltis Christophe Moser | 2018 | Light(Science & Applications)2018，7，1： | 9 |
| 3 | High-fidelity optical diffraction tomography of multiple scattering samples显示文摘We propose an iterative reconstruction scheme for optical diffraction tomography that exploits the split-step nonparaxial(SSNP)method as the forward model in a learning tomography scheme.Compared with the beam propagation method(BPM)previously used in learning tomography(LT-BPM),the improved accuracy of SSNP maximizes the information retrieved from measurements,relying less on prior assumptions about the sample.A rigorous evaluation of learning tomography based on SSNP(LT-SSNP)using both synthetic and experimental measurements confirms its superior performance compared with that of the LT-BPM.Benefiting from the accuracy of SSNP,LT-SSNP can clearly resolve structures that are highly distorted in the LT-BPM.A serious limitation for quantifying the reconstruction accuracy for biological samples is that the ground truth is unknown.To overcome this limitation,we describe a novel method that allows us to compare the performances of different reconstruction schemes by using the discrete dipole approximation to generate synthetic measurements.Finally,we explore the capacity of learning approaches to enable data compression by reducing the number of scanning angles,which is of particular interest in minimizing the measurement time. | Joowon Lim Ahmed B.Ayoub Elizabeth E.Antoine Demetri Psaltis | 2019 | Light(Science & Applications)2019，8，1： | 6 |
| 4 | Physics-informed neural networks for diffraction tomography显示文摘We propose a physics-informed neural network(PINN)as the forward model for tomographic reconstructions of biological samples.We demonstrate that by training this network with the Helmholtz equation as a physical loss,we can predict the scattered field accurately.It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions.We evaluate our methodology with numerical and experimental results.Our PINNs can be generalized for any forward and inverse scattering problem. | Amirhossein Saba Carlo Gigli Ahmed B.Ayoub Demetri Psaltis | 2022 | Advanced Photonics2022，4，6： | 2 |
| 5 | Effect of the oxidation state of LiNbO3:Fe on the diffraction efficiency of multiple holograms显示文摘 | Geoffrey W Burr Demetri Psaltis | 1974 | J Electron Mat1974，，3： | 1 |
| 6 | Insight review: developing optofluidic technology through the fusion of microfluidics and optics 显示文摘 | DEMETRI PSALTIS QUAKE S R | 2006 | Nature2006，442，： | 1 |
| 7 | Position,rotation,and scale invariant optical correlation显示文摘 | David Casasent Demetri Psaltis | 1976 | Applied optics1976，15，7： | 1 |
| 8 | Holographic Memories 显示文摘 | Demetri Psaltis Fai Mok | 1995 | Scientific American1995，11，： | 1 |
| 9 | Multiplexing holo-grams in LiNbO3:Fe:Mncrystals显示文摘 | ADIBI Ali BUSE Karsten PSALTIS Demetri | 1999 | Opt Lett1999，24，10： | 1 |
| 10 | Dense holographic storage promises fast access 显示文摘 | John H Hong Demetri Psaltis | 1996 | Laser Focus World1996，，4： | 1 |
| 11 | Multiplexing holograms in LiNbO3:Fe:Mn crystals显示文摘 | Ali Adibi Karsten Demetri Psaltis | 1999 | Opt Lett1999，24，10： | 1 |
| 12 | Effect of the oxidation state of LiNbO3:Fe on the diffraction efficiency of multiple holograms显示文摘 | Geoffrey W Burr Demetri Psaltis | 1996 | Optics Letters1996，21，12： | 1 |
| 13 | Developing optofluidic technology through the fusion of microfluidics and optics 显示文摘 | Demetri Psaltis Stephen R Quake Changhuei Yang | 2006 | Nature2006，442，27： | 1 |
| 14 | Effect of the oxida- tion state of LiNb03 :Fe on the diffraction efficiency ofmultiple holograms 显示文摘 | Geoffrey W Burr Demetri Psaltis | 1996 | Optics Letters1996，21，： | 1 |
| 15 | Developing Optofluidic Technology through the Fusion of Microfluidies and Optics 显示文摘 | Demetri Psaltis Stephen R Quake Changhuei Yang | 2006 | Nature2006，442，： | 1 |
| 16 | Multiplexing Holograms in LiNbO3:Fe:Mn Crystals 显示文摘 | Ali Adibi Karsten Demetri Psaltis | 1999 | Opt Lett1999，24，10： | 1 |
| 17 | Sensitivity improvement in two-center holographic recording 显示文摘 | Ali Adibi Karsten Buse Demetri Psaltis | 2000 | Opt Lett2000，25，8： | 1 |
| 18 | Imaging in focusing Kerr media using reverse propagation [Invited]显示文摘We present imaging experiments in focusing Kerr media using digital holography and digital reverse propagation(DRP)of the wave.For moderate power,the nonlinear DRP algorithm can be used to improve the quality of images over the linear DRP.We discuss the limits of the method at high power,the role of small-scale filaments,and the problem of time-dependent self-phase modulation. | Alexandre Goy Demetri Psaltis | 2013 | Photonics Research2013，1，2： | 0 |
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