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Fourier ptychographic diffraction tomography (FPDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution and large-field three-dimensional (3D) tomograms by synthesizing a set of low-resolution intensity images obtained with a low numerical aperture (NA) objective. However, in order to ensure sufficient overlap of Ewald spheres in 3D Fourier space, conventional FPDT requires thousands of intensity measurements and consumes a significant amount of time for stable convergence of the iterative algorithm. Herein, we present accelerated Fourier ptychographic diffraction tomography (aFPDT), which combines sparse annular light-emitting diode (LED) illuminations and multiplexing illumination to significantly decrease data amount and achieve computational acceleration of 3D refractive index (RI) tomography. Compared with existing FPDT technique, the equivalent high-resolution 3D RI results are obtained using aFPDT with reducing data requirement by more than 40 times. The validity of the proposed method is experimentally demonstrated on control samples and various biological cells, including polystyrene beads, unicellular algae and clustered HeLa cells in a large field of view. With the capability of high-resolution and high-throughput 3D imaging using small amounts of data, aFPDT has the potential to further advance its widespread applications in biomedicine., (© 2021 Wiley-VCH GmbH.)

Ultrasonic guided waves are useful to assess the integrity of a structure from a remote location. Recently, tomography techniques have been developed to quantitatively estimate the thickness map of plate-like structures based on the dispersion characteristics of guided waves. In many applications only limited locations are available to place transducers. The missing viewing angles lead to artifacts which can degrade the image quality. To address this problem, this paper applies the regularization method to synthesize the missing components. The regularization technique is performed by an adaptive threshold approach to the limited view reconstruction. The effectiveness of this method combined with the full waveform inversion method is demonstrated by using numerical simulations as well as experiments on an irregularly shaped defect and two flat-bottom defects. The results indicate that the additional components obtained from the regularization method can significantly reduce the artifacts, leading to better reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains. The feasibility and generalizability of our network are demonstrated using bacterial cells and a human leukaemic cell line, and by validating the model across different samples. DeepRegularizer offers more than an order of magnitude faster regularization performance compared to the conventional iterative method. We envision that the proposed data-driven approach can bypass the high time complexity of various image reconstructions in other imaging modalities. [ABSTRACT FROM AUTHOR]

A non-destructive iterative interferometric tomographic technique is proposed for imaging fibers with high refractive index contrast, complex structures, and/or large optical path differences. Both simulation and experimental results demonstrate that this technique enables the reconstruction of fiber refractive index profiles accurately and robustly with sub-wavelength resolution. [ABSTRACT FROM AUTHOR]

Quantitative phase imaging (QPI), a method that precisely recovers the wavefront of an electromagnetic field scattered by a transparent, weakly scattering object, is a rapidly growing field of study. By solving the inverse scattering problem, the structure of the scattering object can be reconstructed from QPI data. In the past decade, 3D optical tomographic reconstruction methods based on QPI techniques to solve inverse scattering problems have made significant progress. In this review, we highlight a number of these advances and developments. In particular, we cover in depth Fourier transform light scattering (FTLS), optical diffraction tomography (ODT), and white-light diffraction tomography (WDT). [ABSTRACT FROM AUTHOR]

Tomographic phase microscopy measures the 3-D refractive index distribution of cells and tissues by combining the information from a series of angle-dependent interferometric phase images. In the original device, the frame rate was limited to 0.1 frames per second (fps) by the technique used to acquire phase images, preventing measurements of moving or rapidly changing samples. We describe an improved tomographic phase microscope in which phase images are acquired via a spatial fringe pattern demodulation method, enabling a full tomogram acquisition rate of 30 fps. In addition, in this system the refractive index is calculated by a diffraction tomography algorithm that accounts for the effects of diffraction in the 3-D reconstruction. We use the instrument to quantitatively monitor rapid changes in refractive index within defined subregions of cells due to exposure to acetic acid or changes in medium osmolarity. [ABSTRACT FROM AUTHOR]