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High-fidelity optical diffraction tomography of multiple scattering samples

Light(Science & Applications) 2019年第1期8卷 454-465页

作者： joowon lim Ahmed B.Ayoub Elizabeth E.Antoine Demetri Psaltis Ecole Polytechnique Federale de Lausanne Optics LaboratoryCH-1015 LausanneSwitzerland

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 *** 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 *** 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 *** 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 ***,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.

关键词： scattering optical diffraction

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Three-dimensional tomography of red blood cells using deep learning

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Advanced Photonics 2020年第2期2卷 39-47页

作者： joowon lim Ahmed B.Ayoub Demetri Psaltis Ecole Polytechnique Federale de Lausanne Optics LaboratoryLausanneSwitzerland

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 *** 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 *** 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 *** presented samples are red blood cells(RBCs),which are highly affected by the ill-posed problems due to their *** trained network using synthetic measurements from the digital phantoms successfully eliminates the introduced *** importantly,we obtain high fidelity reconstructions from experimentally recorded projections of real RBC sample using the network that was trained on digitally generated RBC ***,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.

关键词： optical diffraction tomography three-dimensional imaging machine learning deep learning image reconstruction red blood cell missing cone problem

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