Single-cell volumetric imaging with light field microscopy: Advances in systems and algorithms

Single-cell volumetric imaging is essential for researching individual characteristics of cells.As a nonscanning imaging technique,lighteld microscopy(LFM)is a critical tool to achieve realtime three-dimensional imaging with the advantage of single-shot.To address the inherent limits including nonuniform resolution and block-wise artifacts,various modied LFM strategies have been developed to provide new insights into the structural and functional information of cells.This review will introduce the principle and development of LFM,discuss the improved approaches based on hardware designs and 3D reconstruction algorithms,and present the applications in single-cell imaging.

Subvoxel light-sheet microscopy for high-resolution high-throughput volumetric imaging of large biomedical specimens

A key challenge when imaging whole biomedical specimens is how to quickly obtain massive cellular information over a large field of view(FOV).We report a subvoxel light-sheet microscopy(SLSM)method enabling high-throughput volumetric imaging of mesoscale specimens at cellular resolution.A nonaxial,continuous scanning strategy is developed to rapidly acquire a stack of large-FOV images with three-dimensional(3-D)nanoscale shifts encoded.Then,by adopting a subvoxel-resolving procedure,the SLSM method models these low-resolution,cross-correlated images in the spatial domain and can iteratively recover a 3-D image with improved resolution throughout the sample.This technique can surpass the optical limit of a conventional light-sheet microscope by more than three times,with high acquisition speeds of gigavoxels per minute.By fast reconstruction of 3-D cultured cells,intact organs,and live embryos,SLSM method presents a convenient way to circumvent the trade-off between mapping large-scale tissue(>100 mm3)and observing single cell(∼1-μm resolution).It also eliminates the need of complicated mechanical stitching or modulated illumination,using a simple light-sheet setup and fast graphics processing unit-based computation to achieve high-throughput,high-resolution 3-D microscopy,which could be tailored for a wide range of biomedical applications in pathology,histology,neuroscience,etc.

Volumetric Imaging of Neural Activity by Light Field Microscopy

Recording the highly diverse and dynamic activities in large populations of neurons in behaving animals is crucial for a better understanding of how the brain works.To meet this challenge,extensive efforts have been devoted to developing functional fluorescent indicators and optical imaging techniques to optically monitor neural activity.Indeed,optical imaging potentially has extremely high throughput due to its non-invasive access to large brain regions and capability to sample neurons at high density,but the readout speed,such as the scanning speed in two-photon scanning microscopy,is often limited by various practical considerations.Among different imaging methods,light field microscopy features a highly parallelized 3D fluorescence imaging scheme and therefore promises a novel and faster strategy for functional imaging of neural activity.Here,we briefly review the working principles of various types of light field microscopes and their recent developments and applications in neuroscience studies.We also discuss strategies and considerations of optimizing light field microscopy for different experimental purposes,with illustrative examples in imaging zebrafish and mouse brains.

A simple Bessel module with tunable focal depth and constant resolution for commercial two-photon microscope

The volumetric imaging of two-photon microscopy expands the focal depth and improves the throughput,which has unparalleled superiority for three-dimension samples,especially in neuroscience.However,emerging in volumetric imaging is still largely customized,which limits the integration with commercial two-photon systems.Here,we analyzed the key parameters that modulate the focal depth and lateral resolution of polarized annular imaging and proposed a volumetric imaging module that can be directly integrated into commercial two-photon systems using conventional optical elements.This design incorporates the beam diameter adjustment settings of commercial two-photon systems,allowing flexibility to adjust the depth of focus while maintaining the same lateral resolution.Further,the depth range and lateral resolution of the design were verified,and the imaging throughput was demonstrated by an increase in the number of imaging neurons in the awake mouse cerebral cortex.

Theranostics with radiation-induced ultrasound emission(TRUE)

Two novel ultrasound imaging techniques with imaging contrast mechanisms are in the works:X-ray-induced acoustic computed tomography(XACT),and nanoscale photoacoustic tomogra-phy(nPAT).XACT has incredible potential in:(1)biomedical imaging,through which a 3D image can be generated using only a single X-ray projection,and(2)radiation dosimetry.nPATas a new alternative of super-resolution microscopy can break through the optical difraction limitand is capable of exploring sub-celular structures without reliance on fuorescence labeling.We expect these new imaging techniques to find widespread applications in both pre-clinical andclinical biomedical research.

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.

Hyperspectral image classification based on volumetric texture and dimensionality reduction

A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level co-occurrence matrices （VGLCM）. The spectral features were extracted by minimum estimated abundance covar- iance （MEAC） and linear prediction （LP）-based band selection, and a semi-supervised k-means （SKM） cluster- ing method with deleting the worst cluster （SKMd） band- clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classifica- tion by using spectral and textural features. It has been proven that the proposed method using VGLCM outper- forms the gray-level co-occurrence matrices （GLCM） method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can be obtained by iterative optimization methods.In the current work,a practical particle reconstruction method based on a convolutional neural network(CNN)with geometry-informed features is proposed.The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique(ART)based methods.Compared with available ART-based algorithms,the novel technique makes significant improvements in terms of reconstruction quality,robustness to noise,and at least an order of magnitude faster in the offline stage.