Accelerated Fourier ptychographic diffraction tomography with sparse annular LED illuminations

Color-multiplexed 3D differential phase contrast microscopy with optimal annular illumination

Quantitative phase imaging (QPI) has become a valuable tool in the field of biomedical research due to its ability to quantify refractive index variations of live cells and tissues. For example, three-dimensional differential phase contrast (3D DPC) imaging uses through-focus images captured under different illumination patterns deconvoluted with a computed 3D phase transfer function (PTF) to reconstruct the 3D refractive index. In conventional 3D DPC with semi-circular illumination, partially spatially coherent illumination often diminishes phase contrast, exacerbating inherent noise, and can lead to a large number of zero values in the 3D PTF, resulting in strong low-frequency artifacts and deteriorating imaging resolution. To overcome the above drawbacks, we obtain the conditions for acquiring the optimal 3D PTF based on the analysis of the 3D imaging model and the derivation of the 3D PTF calculation process and propose a 3D DPC microscopy based on optimal annular illumination. The proposed optimal annular illumination pattern minimizes the missing frequency components in the 3D Fourier space, resulting in the best noise-robustness and significantly increased phase contrast. To expedite imaging speed, we utilize a 1/2 annular multiplexed illumination, reducing data acquisition volume by 75%. The 3D refractive index tomography of a simulated 3D phase object, unstained tongue sections, and oral epithelial cells demonstrates that our proposed method achieves the above advantages. In conclusion, we demonstrate a novel 3D DPC microscope that only requires replacing the illumination of a commercial microscope with a programmable LED array. The accurate 3D refractive index tomography and the compactness of the system setup allow the method to play a significant role in the biomedical field.

Diagonal illumination scheme for Fourier ptychographic microscopy: resolution doubling and aliasing minimization

Fourier ptychographic microscopy (FPM) is a high-throughput computational imaging technology that enables wide-field and high-resolution imaging of samples with both amplitude and phase information. It holds great promise for quantitative phase imaging (QPI) on a large population of cells in parallel. However, detector undersampling leads to spectrum aliasing, which may significantly degenerate the resolution, efficiency, and quality of QPI, especially when an objective lens with a high space-bandwidth product is used. Here, we introduce a diagonal illumination scheme for FPM to minimize spectrum aliasing, enabling high-resolution QPI under a limited detector sampling rate. By orienting the LED illumination diagonally relative to the detector plane, the non-aliased sampling frequency of the raw image under oblique illumination can be maximized. This illumination scheme, when integrated with a color camera, facilitates single-shot, high-throughput QPI, effectively overcoming spectrum aliasing and achieving incoherent diffraction-limited resolution. Theoretical analysis, simulations, and experiments on resolution target and live cells validate the effectiveness and the proposed illumination scheme, offering a potential guideline for designing an FPM platform for high-speed QPI under the limited detector sampling rates.

Lens-free on-chip 3D microscopy based on wavelength-scanning Fourier ptychographic diffraction tomography

Lens-free on-chip microscopy is a powerful and promising high-throughput computational microscopy technique due to its unique advantage of creating high-resolution images across the full field-of-view (FOV) of the imaging sensor. Nevertheless, most current lens-free microscopy methods have been designed for imaging only two-dimensional thin samples. Lens-free on-chip tomography (LFOCT) with a uniform resolution across the entire FOV and at a subpixel level remains a critical challenge. In this paper, we demonstrated a new LFOCT technique and associated imaging platform based on wavelength scanning Fourier ptychographic diffraction tomography (wsFPDT). Instead of using angularly-variable illuminations, in wsFPDT, the sample is illuminated by on-axis wavelength-variable illuminations, ranging from 430 to 1200 nm. The corresponding under-sampled diffraction patterns are recorded, and then an iterative ptychographic reconstruction procedure is applied to fill the spectrum of the three-dimensional (3D) scattering potential to recover the sample's 3D refractive index (RI) distribution. The wavelength-scanning scheme not only eliminates the need for mechanical motion during image acquisition and precise registration of the raw images but secures a quasi-uniform, pixel-super-resolved imaging resolution across the entire imaging FOV. With wsFPDT, we demonstrate the high-throughput, billion-voxel 3D tomographic imaging results with a half-pitch lateral resolution of 775 nm and an axial resolution of 5.43 μm across a large FOV of 29.85 mm2 and an imaging depth of >200 μm. The effectiveness of the proposed method was demonstrated by imaging various types of samples, including micro-polystyrene beads, diatoms, and mouse mononuclear macrophage cells. The unique capability to reveal quantitative morphological properties, such as area, volume, and sphericity index of single cell over large cell populations makes wsFPDT a powerful quantitative and label-free tool for high-throughput biological applications.

LED-based temporal variant noise model for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a technique to reconstruct a high-resolution image from a set of low-resolution images captured with different illumination angles, which is susceptible to ambient noise, system noise, and weak currents when acquiring large-angle images, especially dark field images. To effectively address the noise problem, we propose an adaptive denoising algorithm based on a LED-based temporal variant noise model. Taking the results of blank slide samples as the reference value of noise, and analyzing the distribution of noise, we establish a statistical model for temporal variant noise, describing the relationship between temporal noise and LED spatial location. Based on this model, Gaussian denoising parameters are selected to adaptively denoise the images with different locations, with which high-resolution images can be reconstructed. Compared with other methods, the experimental results show that the proposed method effectively suppresses the noise, recovers more image details, increases the image contrast, and obtains better visual effects. Meanwhile, better objective evaluation also mirrors the advantages of the proposed algorithms.

Quantitative real-time phase microscopy for extended depth-of-field imaging based on the 3D single-shot differential phase contrast (ssDPC) imaging method

Optical diffraction tomography (ODT) is a promising label-free imaging method capable of quantitatively measuring the three-dimensional (3D) refractive index distribution of transparent samples. In recent years, partially coherent ODT (PC-ODT) has attracted increasing attention due to its system simplicity and absence of laser speckle noise. Quantitative phase imaging (QPI) technologies represented by Fourier ptychographic microscopy (FPM), differential phase contrast (DPC) imaging and intensity diffraction tomography (IDT) need to collect several or hundreds of intensity images, which usually introduce motion artifacts when shooting fast-moving targets, leading to a decrease in image quality. Hence, a quantitative real-time phase microscopy (qRPM) for extended depth of field (DOF) imaging based on 3D single-shot differential phase contrast (ssDPC) imaging method is proposed in this research study. qRPM incorporates a microlens array (MLA) to simultaneously collect spatial information and angular information. In subsequent optical information processing, a deconvolution method is used to obtain intensity stacks under different illumination angles in a raw light field image. Importing the obtained intensity stack into the 3D DPC imaging model is able to finally obtain the 3D refractive index distribution. The captured four-dimensional light field information enables the reconstruction of 3D information in a single snapshot and extending the DOF of qRPM. The imaging capability of the proposed qRPM system is experimental verified on different samples, achieve single-exposure 3D label-free imaging with an extended DOF for 160 µm which is nearly 30 times higher than the traditional microscope system.

Polarization differential interference contrast microscopy with physics-inspired plug-and-play denoiser for single-shot high-performance quantitative phase imaging

We present single-shot high-performance quantitative phase imaging with a physics-inspired plug-and-play denoiser for polarization differential interference contrast (PDIC) microscopy. The quantitative phase is recovered by the alternating direction method of multipliers (ADMM), balancing total variance regularization and a pre-trained dense residual U-net (DRUNet) denoiser. The custom DRUNet uses the Tanh activation function to guarantee the symmetry requirement for phase retrieval. In addition, we introduce an adaptive strategy accelerating convergence and explicitly incorporating measurement noise. After validating this deep denoiser-enhanced PDIC microscopy on simulated data and phantom experiments, we demonstrated high-performance phase imaging of histological tissue sections. The phase retrieval by the denoiser-enhanced PDIC microscopy achieves significantly higher quality and accuracy than the solution based on Fourier transforms or the iterative solution with total variance regularization alone.

Refractive index tomography with a physics-based optical neural network

The non-interference three-dimensional refractive index (RI) tomography has attracted extensive attention in the life science field for its simple system implementation and robust imaging performance. However, the complexity inherent in the physical propagation process poses significant challenges when the sample under study deviates from the weak scattering approximation. Such conditions complicate the task of achieving global optimization with conventional algorithms, rendering the reconstruction process both time-consuming and potentially ineffective. To address such limitations, this paper proposes an untrained multi-slice neural network (MSNN) with an optical structure, in which each layer has a clear corresponding physical meaning according to the beam propagation model. The network does not require pre-training and performs good generalization and can be recovered through the optimization of a set of intensity images. Concurrently, MSNN can calibrate the intensity of different illumination by learnable parameters, and the multiple backscattering effects have also been taken into consideration by integrating a "scattering attenuation layer" between adjacent "RI" layers in the MSNN. Both simulations and experiments have been conducted carefully to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that MSNN can enhance clarity with increased efficiency in RI tomography. The implementation of MSNN introduces a novel paradigm for RI tomography.

Polarization-sensitive intensity diffraction tomography

Optical anisotropy, which is an intrinsic property of many materials, originates from the structural arrangement of molecular structures, and to date, various polarization-sensitive imaging (PSI) methods have been developed to investigate the nature of anisotropic materials. In particular, the recently developed tomographic PSI technologies enable the investigation of anisotropic materials through volumetric mappings of the anisotropy distribution of these materials. However, these reported methods mostly operate on a single scattering model, and are thus not suitable for three-dimensional (3D) PSI imaging of multiple scattering samples. Here, we present a novel reference-free 3D polarization-sensitive computational imaging technique-polarization-sensitive intensity diffraction tomography (PS-IDT)-that enables the reconstruction of 3D anisotropy distribution of both weakly and multiple scattering specimens from multiple intensity-only measurements. A 3D anisotropic object is illuminated by circularly polarized plane waves at various illumination angles to encode the isotropic and anisotropic structural information into 2D intensity information. These information are then recorded separately through two orthogonal analyzer states, and a 3D Jones matrix is iteratively reconstructed based on the vectorial multi-slice beam propagation model and gradient descent method. We demonstrate the 3D anisotropy imaging capabilities of PS-IDT by presenting 3D anisotropy maps of various samples, including potato starch granules and tardigrade.