scholarly journals Imaging biological tissue with high-throughput single-pixel compressive holography

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Daixuan Wu ◽  
Jiawei Luo ◽  
Guoqiang Huang ◽  
Yuanhua Feng ◽  
Xiaohua Feng ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Spatial Information ◽  
Biological Tissues ◽  
Large Field ◽  
Pixel Detector ◽  
Array Detectors ◽  
Pixel Array ◽  
Compressive Holography ◽  
Single Pixel

AbstractSingle-pixel holography (SPH) is capable of generating holographic images with rich spatial information by employing only a single-pixel detector. Thanks to the relatively low dark-noise production, high sensitivity, large bandwidth, and cheap price of single-pixel detectors in comparison to pixel-array detectors, SPH is becoming an attractive imaging modality at wavelengths where pixel-array detectors are not available or prohibitively expensive. In this work, we develop a high-throughput single-pixel compressive holography with a space-bandwidth-time product (SBP-T) of 41,667 pixels/s, realized by enabling phase stepping naturally in time and abandoning the need for phase-encoded illumination. This holographic system is scalable to provide either a large field of view (~83 mm2) or a high resolution (5.80 μm × 4.31 μm). In particular, high-resolution holographic images of biological tissues are presented, exhibiting rich contrast in both amplitude and phase. This work is an important step towards multi-spectrum imaging using a single-pixel detector in biophotonics.

Imaging biological tissue with high-throughput single-pixel compressive holography

2021 ◽  
Author(s):  
Wu Daixuan ◽  
Luo Jiawei ◽  
Huang Guoqiang ◽  
Yuanhua Feng ◽  
Feng Xiaohua ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Biological Tissue ◽  
Current Practice ◽  
Pixel Detector ◽  
Phase Stepping ◽  
Array Detectors ◽  
Pixel Array ◽  
Compressive Holography ◽  
Single Pixel

Abstract Single-pixel holography (SPH) is capable of generating holographic images with rich spatial information by employing only a single-pixel detector. Thanks to the relatively low dark-noise production, high sensitivity, large bandwidth, and cheap price of single-pixel detectors in comparison to pixel-array detectors, SPH is becoming an attractive imaging modality at wavelengths where pixel-array detectors are not available or prohibitively expensive. Moreover, SPH is particularly advantageous when imaging through scattering media or in scarce illumination with compressive sensing. In the current practice of SPH, the throughput of the system is mainly limited by the phase-encoded illumination and the ways to realize phase stepping. In this work, we developed a high-through single-pixel compressive holography, achieving a space-bandwidth-time product (SBP-T) of 41,667 pixels/s. This result indicates that by using a single-pixel detector, information of holographic images containing up to 65,536 pixels can be collected within only 3 seconds. The high-throughput was realized by enabling phase stepping naturally in time and abandoning the need for phase-encoded illumination. We further show that compressive sensing can be conveniently adapted to significantly reduce the acquisition time. Besides being high throughput, we also show that this holographic system is scalable to provide either a large field of view (~83 mm2) or a high resolution (5.8 μm × 4.3 μm). In particular, high-resolution holographic images of a piece of rat tail were presented, exhibiting rich information of mussel, cortical bone, and cancellous bone. Given that microscopic images of biological tissue has rarely been explored in the current practice of SPH, we anticipate the developed high-throughput SPH is promising to nourish the development of multi-spectrum imaging by providing high-quality holographic images for biological tissues.

scholarly journals More from less: high-throughput dual polarity lipid imaging of biological tissues

The Analyst ◽  
2016 ◽  
Vol 141 (12) ◽  
pp. 3832-3841 ◽  
Author(s):  
Shane R. Ellis ◽  
Joanna Cappell ◽  
Nina Ogrinc Potočnik ◽  
Benjamin Balluff ◽  
Julie Hamaide ◽  
...  
Keyword(s):  
High Throughput ◽  
High Speed ◽  
Spatial Information ◽  
Biological Tissues ◽  
Tissue Sections ◽  
Dual Polarity

Here, we reveal the increased biochemical and spatial information acquired using high-speed MALDI-MSI and sequential acquisitions of positive and negative lipid-MSI data from single tissue sections.

scholarly journals Sub-voxel light-sheet microscopy for high-resolution, high-throughput volumetric imaging of large biomedical specimens

2018 ◽  
Author(s):  
Peng Fei ◽  
Jun Nie ◽  
Juhyun Lee ◽  
Yichen Ding ◽  
Shuoran Li ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Light Sheet ◽  
Large Field ◽  
Mouse Heart ◽  
Processing Unit ◽  
Intact Mouse ◽  
Volumetric Imaging ◽  
Light Sheet Microscopy ◽  
Wide Range

A key challenge when imaging whole biomedical specimens is how to quickly obtain massive cellular information over a large field of view (FOV). Here, we report a sub-voxel light-sheet microscopy (SLSM) method enabling high-throughput volumetric imaging of mesoscale specimens at cellular-resolution. A non-axial, continuous scanning strategy is used to rapidly acquire a stack of large-FOV images with three-dimensional (3-D) nanoscale shifts encoded. Then by adopting a sub-voxel-resolving procedure, the SLSM method models these low-resolution, cross-correlated images in the spatial domain and iteratively recovers 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. As demonstrated by quick reconstruction (minutes to hours) of various samples, e.g., 3-D cultured cells, an intact mouse heart, mouse brain, and live zebrafish embryo, the SLSM method presents a high-throughput way to circumvent the tradeoff between intoto mapping of large-scale tissue (>100 mm3) and isotropic imaging of single-cell (~1-μm resolution). It also eliminates the need of complicated mechanical stitching or precisely modulated illumination, using a simple light-sheet setup and fast graphics-processing-unit (GPU)-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.

scholarly journals Deep learning based high-resolution incoherent x-ray imaging with a single-pixel detector

2020 ◽  
Author(s):  
Yu-Hang He ◽  
Ai-Xin Zhang ◽  
Ming-Fei Li ◽  
Yi-Yi Huang ◽  
Bao-Gang Quan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Pixel Detector ◽  
X Ray ◽  
X Ray Imaging ◽  
Single Pixel

scholarly journals Non-Scanning Three-Dimensional Imaging System with a Single-Pixel Detector: Simulation and Experimental Study

2020 ◽  
Vol 10 (9) ◽  
pp. 3100
Author(s):  
Guang Shi ◽  
Leijue Zheng ◽  
Wen Wang ◽  
Keqing Lu
Keyword(s):  
3D Imaging ◽  
Imaging System ◽  
Three Dimensional ◽  
Scanning Laser ◽  
Pixel Detector ◽  
Reconstruction Algorithms ◽  
Imaging Technology ◽  
Area Array ◽  
Array Detectors ◽  
Single Pixel

Existing scanning laser three-dimensional (3D) imaging technology has slow measurement speed. In addition, the measurement accuracy of non-scanning laser 3D imaging technology based on area array detectors is limited by the resolution and response frequency of area array detectors. As a result, applications of laser 3D imaging technology are limited. This paper completed simulations and experiments of a non-scanning 3D imaging system with a single-pixel detector. The single-pixel detector can be used to achieve 3D imaging of a target by compressed sensing to overcome the shortcomings of the existing laser 3D imaging technology. First, the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the measurement results were compared through simulation experiments. Second, a non-scanning 3D imaging experimental platform was designed and constructed. Finally, an experiment was performed to compare the effects of different sampling rates and reconstruction algorithms on the reconstruction effect of 3D imaging to obtain a 3D image with a resolution of 8 × 8. The simulation results show that the reconstruction effect of the Hadamard measurement matrix and the minimum total variation reconstruction algorithm performed well.

scholarly journals Computational optical imaging with a photonic lantern

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Debaditya Choudhury ◽  
Duncan K. McNicholl ◽  
Audrey Repetti ◽  
Itandehui Gris-Sánchez ◽  
Shuhui Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Mode ◽  
Computational Imaging ◽  
Optical Fibres ◽  
Biological Processes ◽  
Pixel Detector ◽  
Imaging Algorithm ◽  
Single Pixel ◽  
The Ideal

Abstract The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode “photonic lantern” transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.

scholarly journals Compressive holography with a single-pixel detector

2013 ◽  
Vol 38 (14) ◽  
pp. 2524 ◽  
Author(s):  
Pere Clemente ◽  
Vicente Durán ◽  
Enrique Tajahuerce ◽  
Pedro Andrés ◽  
Vicent Climent ◽  
...  
Keyword(s):  
Pixel Detector ◽  
Compressive Holography ◽  
Single Pixel

scholarly journals Efficient Fourier Single-Pixel Imaging with Gaussian Random Sampling

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 319
Author(s):  
Ziheng Qiu ◽  
Xinyi Guo ◽  
Tian’ao Lu ◽  
Pan Qi ◽  
Zibang Zhang ◽  
...  
Keyword(s):  
Fourier Coefficients ◽  
Fourier Spectrum ◽  
Spatial Information ◽  
Imaging Techniques ◽  
Gaussian Function ◽  
Sampling Strategy ◽  
Acquisition Time ◽  
Pixel Detector ◽  
Structured Illumination ◽  
Single Pixel

Fourier single-pixel imaging (FSI) is a branch of single-pixel imaging techniques. It allows any image to be reconstructed by acquiring its Fourier spectrum by using a single-pixel detector. FSI uses Fourier basis patterns for structured illumination or structured detection to acquire the Fourier spectrum of image. However, the spatial resolution of the reconstructed image mainly depends on the number of Fourier coefficients sampled. The reconstruction of a high-resolution image typically requires a number of Fourier coefficients to be sampled. Consequently, a large number of single-pixel measurements lead to a long data acquisition time, resulting in imaging of a dynamic scene challenging. Here we propose a new sampling strategy for FSI. It allows FSI to reconstruct a clear and sharp image with a reduced number of measurements. The key to the proposed sampling strategy is to perform a density-varying sampling in the Fourier space and, more importantly, the density with respect to the importance of Fourier coefficients is subject to a one-dimensional Gaussian function. The final image is reconstructed from the undersampled Fourier spectrum through compressive sensing. We experimentally demonstrate the proposed method is able to reconstruct a sharp and clear image of 256 × 256 pixels with a sampling ratio of 10%. The proposed method enables fast single-pixel imaging and provides a new approach for efficient spatial information acquisition.

Ghost cytometry

Science ◽  
2018 ◽  
Vol 360 (6394) ◽  
pp. 1246-1251 ◽  
Author(s):  
Sadao Ota ◽  
Ryoichi Horisaki ◽  
Yoko Kawamura ◽  
Masashi Ugawa ◽  
Issei Sato ◽  
...  
Keyword(s):  
Cell Morphology ◽  
Spatial Information ◽  
Random Pattern ◽  
Pixel Detector ◽  
Ghost Imaging ◽  
Machine Learning Methods ◽  
Imaging Cytometry ◽  
Temporal Waveform ◽  
Single Pixel ◽  
Optical Structure

Ghost imaging is a technique used to produce an object’s image without using a spatially resolving detector. Here we develop a technique we term “ghost cytometry,” an image-free ultrafast fluorescence “imaging” cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.

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