Quantitative damage imaging using Lamb wave diffraction tomography

In this paper, we investigate the diffraction tomography for quantitative imaging damages of partly through-thickness holes with various shapes in isotropic plates by using converted and non-converted scattered Lamb waves generated nu- merically. Finite element simulations are carried out to provide the scattered wave data. The validity of the finite element model is confirmed by the comparison of scattering directivity pattern （SDP） of circle blind hole damage between the finite element simulations and the analytical results. The imaging method is based on a theoretical relation between the one-dimensional （1D） Fourier transform of the scattered projection and two-dimensional （2D） spatial Fourier transform of the scattering object. A quantitative image of the damage is obtained by carrying out the 2D inverse Fourier transform of the scattering object. The proposed approach employs a circle transducer network containing forward and backward projections, which lead to so-called transmission mode （TMDT） and reflection mode diffraction tomography （RMDT）, respectively. The reconstructed results of the two projections for a non-converted SO scattered mode are investigated to illuminate the influence of the scattering field data. The results show that Lamb wave diffraction tomography using the combination of TMDT and RMDT improves the imaging effect compared with by using only the TMDT or RMDT. The scattered data of the converted A0 mode are also used to assess the performance of the diffraction tomography method. It is found that the circle and elliptical shaped damages can still be reasonably identified from the reconstructed images while the reconstructed results of other complex shaped damages like crisscross rectangles and racecourse are relatively poor.

Physics-informed neural networks for diffraction tomography

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.

High-speed in vitro intensity diffraction tomography

We demonstrate a label-free,scan-free intensity diffraction tomography technique utilizing annular illumination(aIDT)to rapidly characterize large-volume three-dimensional(3-D)refractive index distributions in vitro.By optimally matching the illumination geometry to the microscope pupil,our technique reduces the data requirement by 60 times to achieve high-speed 10-Hz volume rates.Using eight intensity images,we recover volumes of∼350μm×100μm×20μm,with near diffraction-limited lateral resolution of∼487 nm and axial resolution of∼3.4μm.The attained large volume rate and high-resolution enable 3-D quantitative phase imaging of complex living biological samples across multiple length scales.We demonstrate aIDT’s capabilities on unicellular diatom microalgae,epithelial buccal cell clusters with native bacteria,and live Caenorhabditis elegans specimens.Within these samples,we recover macroscale cellular structures,subcellular organelles,and dynamic micro-organism tissues with minimal motion artifacts.Quantifying such features has significant utility in oncology,immunology,and cellular pathophysiology,where these morphological features are evaluated for changes in the presence of disease,parasites,and new drug treatments.Finally,we simulate the aIDT system to highlight the accuracy and sensitivity of the proposed technique.aIDT shows promise as a powerful high-speed,label-free computational microscopy approach for applications where natural imaging is required to evaluate environmental effects on a sample in real time.

Continuous-wave terahertz diffraction tomography for measuring three-dimensional refractive index maps

Three-dimensional(3D)refractive index(RI)distribution is important to reveal the object’s inner structure.We implemented terahertz(THz)diffraction tomography with a continuous-wave single-frequency THz source for measuring 3D RI maps.The off-axis holographic interference configuration was employed to obtain the quantitative scattered field of the object under each rotation angle.The 3D reconstruction algorithm adopted the filtered backpropagation method,which can theoretically calculate the exact scattering potential from the measured scattered field.Based on the Rytov approximation,the 3D RI distribution of polystyrene foam spheres was achieved with high fidelity,which verified the feasibility of the proposed method.

Digital Imaging Reconstruction from Multiple Angle Diversity Using Digital Filtering Technique

Microwave diffraction tomography is a process to infer the internal structure of an objectfrom multiple angle views of microwave diffraction shadow. Being sensitive to variations in refractive index of the object, the procedure can be used to measure permittivity distributions within dielectric objects and to image soft tissues for biomedical applications. The optimal resolution distance obtainable is half a wavelength, but this can rarely be achieved because of practical limitations. Some procedures, however, are available to improve the practical resolution. One, which is suitable for microwave tomography, is to use multiple angle views data and to combine the resulting images. The other, which is suitable for improving the image reconstruction resolution, is to use the digital filtering technique and the filtered backpropagation algorithm. A system operating over the X-band microwave frequency is described and some experimental results for objects in air are given.

Three-dimensional tomography of red blood cells using deep learning

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.

Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution

Histopathology relies upon the staining and sectioning of biological tissues,which can be laborious and may cause artifacts and distort tissues.We develop label-free volumetric imaging of thick-tissue slides,exploiting refractive index distributions as intrinsic imaging contrast.The present method systematically exploits label-free quantitative phase imaging techniques,volumetric reconstruction of intrinsic refractive index distributions in tissues,and numerical algorithms for the seamless stitching of multiple three-dimensional tomograms and for reducing scattering-induced image distortion.We demonstrated label-free volumetric imaging of thick tissues with the field of view of 2 mm×1.75 mm×0.2 mm with a spatial resolution of 170 nm×170 nm×1400 nm.The number of optical modes,calculated as the reconstructed volume divided by the size of the point spread function,was∼20 giga voxels.We have also demonstrated that different tumor types and a variety of precursor lesions and pathologies can be visualized with the present method.

Seismic-Wave Scattering,Imaging,and Inversion

Ru-Shan Wu has made seminal contributions in many research areas in geophysics,such as seismic-wave propagation,scattering,imaging,and inversion.We highlight some of his research in holography imaging,diffraction tomography,seismic-wave scattering and its applications to studying Earth’s heterogeneity,oneway wave propagation and one-return wave modeling,beamlet and dreamlet applications,strong non-linear full-waveform inversion,and direct envelop inversion.