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 reconstruc...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.展开更多
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 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.展开更多
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 propaga...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.展开更多
Direct laser writing(DLW)enables arbitrary three-dimensional nanofabrication.However,the diffraction limit poses a major obstacle for realizing nanometer-scale features.Furthermore,it is challenging to improve the fab...Direct laser writing(DLW)enables arbitrary three-dimensional nanofabrication.However,the diffraction limit poses a major obstacle for realizing nanometer-scale features.Furthermore,it is challenging to improve the fabrication efficiency using the currently prevalent single-focal-spot systems,which cannot perform high-throughput lithography.To overcome these challenges,a parallel peripheral-photoinhibition lithography system with a sub-40-nm two-dimensional feature size and a sub-20-nm suspended line width was developed in our study,based on two-photon polymerization DLW.The lithography efficiency of the developed system is twice that of conventional systems for both uniform and complex structures.The proposed system facilitates the realization of portable DLW with a higher resolution and throughput.展开更多
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.
基金the Swiss National Science Foundation(SNSF)under funding number 514481.
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.
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.
基金the National Key Research and Development Program of China(Grant No.2021YFF0502700)the National Natural Science Foundation of China(Grant Nos.62105298,52105565,and 22105180)+2 种基金China Postdoctoral Science Foundation(Grant Nos.2020M671823 and 2020M681956)the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LD21F050002,LQ22F050017,and LQ22F050015)the Major Scientific Project of Zhejiang Lab,China(Grant No.2020MC0AE01).
文摘Direct laser writing(DLW)enables arbitrary three-dimensional nanofabrication.However,the diffraction limit poses a major obstacle for realizing nanometer-scale features.Furthermore,it is challenging to improve the fabrication efficiency using the currently prevalent single-focal-spot systems,which cannot perform high-throughput lithography.To overcome these challenges,a parallel peripheral-photoinhibition lithography system with a sub-40-nm two-dimensional feature size and a sub-20-nm suspended line width was developed in our study,based on two-photon polymerization DLW.The lithography efficiency of the developed system is twice that of conventional systems for both uniform and complex structures.The proposed system facilitates the realization of portable DLW with a higher resolution and throughput.
