Near-common-path interferometer for imaging Fourier-transform spectroscopy in wide-field microscopy

Sequency encoding single pixel spectroscopy based on Hadamard transform

Single pixel spectroscopy based on Hadamard transform (SPS-HT) has been applied widely because of its capability of wavelength multiplexing and associated advantage in signal-to-noise ratio. In this paper, we propose a sequency encoding single pixel spectroscopy (SESPS) based on two-dimensional (2D) masks for concurrent coding of all Hadamard coefficients instead of one-dimensional (1D) Hadamard masks (only coding one coefficient at a time) widely used in the traditional SPS-HT. Moreover, each Hadamard coefficient is coded along the time dimension with a different sequency value such that the alternating current (AC) measurements of the time-domain signal can be used to reconstruct all Hadamard coefficients simultaneously, which reduces the influence of noise and dramatically speeds up data acquisition. We demonstrate that the SESPS with 32 spectral channels can accelerate spectral measurements from white light sources and fluorescence particles by around 14 times and 70 times, respectively, compared to measurements using a commercial spectrometer when the relative root mean square error (RMSE) is around 3% or smaller. The acceleration factors can be boosted by an extra 4 times when only eight spectral channels are used to achieve a compression ratio of 4:1, in which the relative RMSEs change only marginally. Compared to our previous SPS-HT, this new scheme can increase the speed by three orders of magnitude. This technique is expected to be useful in applications requiring high-speed spectral measurements such as the spectral flow cytometry and on-site medical diagnosis using fluorescence or Raman spectroscopy.

Multi-slice ptychographic imaging with multistage coarse-to-fine reconstruction

The ability to image 3D samples with optical sectioning is essential for the study of tomographic morphology in material and biological sciences. However, it is often hampered by limitations of acquisition speed and equipment complexity when performing 3D volumetric imaging. Here, we propose, to the best of our knowledge, a new method for 3D reconstruction from a minimum of four intensity-only measurements. The complementary structured patterns provided by the digital micromirror device (DMD) irradiate the outermost layer of the sample to generate the corresponding diffraction intensities for recording, which enables rapid scanning of loaded patterns for fast acquisition. Our multistage reconstruction algorithm first extracts the overall coarse-grained information, and then iteratively optimizes the information of different layers to obtain fine features, thereby achieving high-resolution 3D tomography. The high-fidelity reconstruction in experiments on two-slice resolution targets, unstained Polyrhachis vicina Roger and freely moving C. elegans proves the robustness of the method. Compared with traditional 3D reconstruction methods such as interferometry-based methods or Fourier ptychographic tomography (FPT), our method increases the reconstruction speed by at least 10 times and is suitable for label-free dynamic imaging in multiple-scattering samples. Such 3D reconstruction suggests potential applications in a wide range of fields.

Deep-learning-assisted Fourier transform imaging spectroscopy for hyperspectral fluorescence imaging

Hyperspectral fluorescence imaging is widely used when multiple fluorescent probes with close emission peaks are required. In particular, Fourier transform imaging spectroscopy (FTIS) provides unrivaled spectral resolution; however, the imaging throughput is very low due to the amount of interferogram sampling required. In this work, we apply deep learning to FTIS and show that the interferogram sampling can be drastically reduced by an order of magnitude without noticeable degradation in the image quality. For the demonstration, we use bovine pulmonary artery endothelial cells stained with three fluorescent dyes and 10 types of fluorescent beads with close emission peaks. Further, we show that the deep learning approach is more robust to the translation stage error and environmental vibrations. Thereby, the He-Ne correction, which is typically required for FTIS, can be bypassed, thus reducing the cost, size, and complexity of the FTIS system. Finally, we construct neural network models using Hyperband, an automatic hyperparameter selection algorithm, and compare the performance with our manually-optimized model.

Compressed sensing in fluorescence microscopy

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.

Phasor-based hyperspectral snapshot microscopy allows fast imaging of live, three-dimensional tissues for biomedical applications

Hyperspectral imaging is highly sought after in many fields including mineralogy and geology, environment and agriculture, astronomy and, importantly, biomedical imaging and biological fluorescence. We developed ultrafast phasor-based hyperspectral snapshot microscopy based on sine/cosine interference filters for biomedical imaging not feasible with conventional hyperspectral detection methods. Current approaches rely on slow spatial or spectral scanning limiting their application in living biological tissues, while faster snapshot methods such as image mapping spectrometry and multispectral interferometry are limited in spatial and/or spectral resolution, are computationally demanding, and imaging devices are very expensive to manufacture. Leveraging light sheet microscopy, phasor-based hyperspectral snapshot microscopy improved imaging speed 10-100 fold which, combined with minimal light exposure and high detection efficiency, enabled hyperspectral metabolic imaging of live, three-dimensional mouse tissues not feasible with other methods. As a fit-free method that does not require any a priori information often unavailable in complex and evolving biological systems, the rule of linear combinations of the phasor could spectrally resolve subtle differences between cell types in the developing zebrafish retina and spectrally separate and track multiple organelles in 3D cultured cells over time. The sine/cosine snapshot method is adaptable to any microscope or imaging device thus making hyperspectral imaging and fit-free analysis based on linear combinations broadly available to researchers and the public.

Line-scan compressive Raman imaging with spatiospectral encoding

We report a line-scanning imaging modality of compressive Raman technology with a single-pixel detector. The spatial information along the illumination line is encoded onto one axis of a digital micromirror device, while spectral coding masks are applied along the orthogonal direction. We demonstrate imaging and classification of three different chemical species.

Optical elastography and tissue biomechanics

Mechanical forces play an important role in the behavior and development of biological systems and disease at all spatial scales, from cells and their constituents to tissues and organs. Such forces have a profound influence on the health, structural integrity, and normal function of cells and organs. Accurate knowledge of cell and tissue biomechanical properties is essential to map the distribution of forces and mechanical cues in biological systems. Cell and tissue biomechanical properties are also known to be important on their own as indicators of health or diseases state. Hence, optical elastography and biomechanics methods can aid in the understanding and clinical diagnosis of a wide variety of diseases. We provide a brief overview and highlight of the Optical Elastography and Tissue Biomechanics VI conference, which took place in San Francisco, February 2 and 3, 2019, as a part of Photonics West symposium.

Compressive Raman imaging with spatial frequency modulated illumination

We report a line scanning imaging modality of compressive Raman technology with spatial frequency modulated illumination using a single pixel detector. We demonstrate the imaging and classification of three different chemical species at line scan rates of 40 Hz.