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Luo X, Lu Z, Jin M, Chen S, Yang J. Efficient high-resolution fluorescence projection imaging over an extended depth of field through optical hardware and deep learning optimizations. BIOMEDICAL OPTICS EXPRESS 2024; 15:3831-3847. [PMID: 38867796 PMCID: PMC11166417 DOI: 10.1364/boe.523312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/27/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024]
Abstract
Optical microscopy has witnessed notable advancements but has also become more costly and complex. Conventional wide field microscopy (WFM) has low resolution and shallow depth-of-field (DOF), which limits its applications in practical biological experiments. Recently, confocal and light sheet microscopy become major workhorses for biology that incorporate high-precision scanning to perform imaging within an extended DOF but at the sacrifice of expense, complexity, and imaging speed. Here, we propose deep focus microscopy, an efficient framework optimized both in hardware and algorithm to address the tradeoff between resolution and DOF. Our deep focus microscopy achieves large-DOF and high-resolution projection imaging by integrating a deep focus network (DFnet) into light field microscopy (LFM) setups. Based on our constructed dataset, deep focus microscopy features a significantly enhanced spatial resolution of ∼260 nm, an extended DOF of over 30 µm, and broad generalization across diverse sample structures. It also reduces the computational costs by four orders of magnitude compared to conventional LFM technologies. We demonstrate the excellent performance of deep focus microscopy in vivo, including long-term observations of cell division and migrasome formation in zebrafish embryos and mouse livers at high resolution without background contamination.
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Affiliation(s)
- Xin Luo
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Zhi Lu
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Manchang Jin
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Shuai Chen
- Department of Gastroenterology and Hepatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingyu Yang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
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2
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Shi W, Quan H, Kong L. High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion. OPTICS EXPRESS 2024; 32:3710-3722. [PMID: 38297586 DOI: 10.1364/oe.510728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024]
Abstract
The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system. By introducing the surface normal vector information obtained from polarization measurement and integrating it with the light-field 3D point cloud data, 3D reconstruction results accuracy is highly improved in axial direction. Experimental results with a Fourier light-field 3D imaging microscope demonstrated a substantial enhancement of vertical resolution with a depth resolution to depth of field ratio of 0.19%. This represented approximately 44 times the improvement compared to the theoretical ratio before data fusion, enabling the system to access more detailed information with finer measurement accuracy for test samples. This work not only provides a feasible solution for breaking the limitations imposed by traditional light-field microscope hardware configurations but also offers superior 3D measurement approach in a more cost-effective and practical manner.
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Taylor JM. Fast algorithm for 3D volume reconstruction from light field microscopy datasets. OPTICS LETTERS 2023; 48:4177-4180. [PMID: 37581986 DOI: 10.1364/ol.490061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/05/2023] [Indexed: 08/17/2023]
Abstract
Light field microscopy can capture 3D volume datasets in a snapshot, making it a valuable tool for high-speed 3D imaging of dynamic biological events. However, subsequent computational reconstruction of the raw data into a human-interpretable 3D+time image is very time-consuming, limiting the technique's utility as a routine imaging tool. Here we derive improved equations for 3D volume reconstruction from light field microscopy datasets, leading to dramatic speedups. We characterize our open-source Python implementation of these algorithms and demonstrate real-world reconstruction speedups of more than an order of magnitude compared with established approaches. The scale of this performance improvement opens up new possibilities for studying large timelapse datasets in light field microscopy.
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Lu Z, Liu Y, Jin M, Luo X, Yue H, Wang Z, Zuo S, Zeng Y, Fan J, Pang Y, Wu J, Yang J, Dai Q. Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging. Nat Methods 2023; 20:735-746. [PMID: 37024654 PMCID: PMC10172145 DOI: 10.1038/s41592-023-01839-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/07/2023] [Indexed: 04/08/2023]
Abstract
High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.
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Affiliation(s)
- Zhi Lu
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Yu Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Manchang Jin
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Xin Luo
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Huanjing Yue
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Zian Wang
- Department of Automation, Tsinghua University, Beijing, China
| | - Siqing Zuo
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Yunmin Zeng
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Jiaqi Fan
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Yanwei Pang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
| | - Jingyu Yang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
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Cui Y, Zhang X, Li X, Lin J. Multiscale microscopy to decipher plant cell structure and dynamics. THE NEW PHYTOLOGIST 2023; 237:1980-1997. [PMID: 36477856 DOI: 10.1111/nph.18641] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
New imaging methodologies with high contrast and molecular specificity allow researchers to analyze dynamic processes in plant cells at multiple scales, from single protein and RNA molecules to organelles and cells, to whole organs and tissues. These techniques produce informative images and quantitative data on molecular dynamics to address questions that cannot be answered by conventional biochemical assays. Here, we review selected microscopy techniques, focusing on their basic principles and applications in plant science, discussing the pros and cons of each technique, and introducing methods for quantitative analysis. This review thus provides guidance for plant scientists in selecting the most appropriate techniques to decipher structures and dynamic processes at different levels, from protein dynamics to morphogenesis.
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Affiliation(s)
- Yaning Cui
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, 100083, China
- College of Biological Sciences & Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xi Zhang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, 100083, China
- College of Biological Sciences & Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xiaojuan Li
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, 100083, China
- College of Biological Sciences & Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Jinxing Lin
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, 100083, China
- College of Biological Sciences & Biotechnology, Beijing Forestry University, Beijing, 100083, China
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Cai Y, Wu J, Dai Q. Review on data analysis methods for mesoscale neural imaging in vivo. NEUROPHOTONICS 2022; 9:041407. [PMID: 35450225 PMCID: PMC9010663 DOI: 10.1117/1.nph.9.4.041407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.
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Affiliation(s)
- Yeyi Cai
- Tsinghua University, Department of Automation, Beijing, China
| | - Jiamin Wu
- Tsinghua University, Department of Automation, Beijing, China
| | - Qionghai Dai
- Tsinghua University, Department of Automation, Beijing, China
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Zhai J, Shi R, Fan K, Kong L. Background inhibited and speed-loss-free volumetric imaging in vivo based on structured-illumination Fourier light field microscopy. Front Neurosci 2022; 16:1004228. [PMID: 36248666 PMCID: PMC9558295 DOI: 10.3389/fnins.2022.1004228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Benefiting from its advantages in fast volumetric imaging for recording biodynamics, Fourier light field microscopy (FLFM) has a wide range of applications in biomedical research, especially in neuroscience. However, the imaging quality of the FLFM is always deteriorated by both the out-of-focus background and the strong scattering in biological samples. Here we propose a structured-illumination and interleaved-reconstruction based Fourier light field microscopy (SI-FLFM), in which we can filter out the background fluorescence in FLFM without sacrificing imaging speed. We demonstrate the superiority of our SI-FLFM in high-speed, background-inhibited volumetric imaging of various biodynamics in larval zebrafish and mice in vivo. The signal-to-background ratio (SBR) is improved by tens of times. And the volumetric imaging speed can be up to 40 Hz, avoiding artifacts caused by temporal under-sampling in conventional structured illumination microscopy. These suggest that our SI-FLFM is suitable for applications of weak fluorescence signals but high imaging speed requirements.
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Affiliation(s)
- Jiazhen Zhai
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Ruheng Shi
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Kuikui Fan
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Lingjie Kong
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- *Correspondence: Lingjie Kong,
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Ferdman B, Saguy A, Xiao D, Shechtman Y. Diffractive optical system design by cascaded propagation. OPTICS EXPRESS 2022; 30:27509-27530. [PMID: 36236921 DOI: 10.1364/oe.465230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 06/30/2022] [Indexed: 06/16/2023]
Abstract
Modern design of complex optical systems relies heavily on computational tools. These frequently use geometrical optics as well as Fourier optics. Fourier optics is typically used for designing thin diffractive elements, placed in the system's aperture, generating a shift-invariant Point Spread Function (PSF). A major bottleneck in applying Fourier Optics in many cases of interest, e.g. when dealing with multiple, or out-of-aperture elements, comes from numerical complexity. In this work, we propose and implement an efficient and differentiable propagation model based on the Collins integral, which enables the optimization of diffractive optical systems with unprecedented design freedom using backpropagation. We demonstrate the applicability of our method, numerically and experimentally, by engineering shift-variant PSFs via thin plate elements placed in arbitrary planes inside complex imaging systems, performing cascaded optimization of multiple planes, and designing optimal machine-vision systems by deep learning.
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Zhu T, Guo Y, Zhang Y, Lu Z, Lin X, Fang L, Wu J, Dai Q. Noise-robust phase-space deconvolution for light-field microscopy. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:076501. [PMID: 35883238 PMCID: PMC9319196 DOI: 10.1117/1.jbo.27.7.076501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Light-field microscopy has achieved success in various applications of life sciences that require high-speed volumetric imaging. However, existing light-field reconstruction algorithms degrade severely in low-light conditions, and the deconvolution process is time-consuming. AIM This study aims to develop a noise robustness phase-space deconvolution method with low computational costs. APPROACH We reformulate the light-field phase-space deconvolution model into the Fourier domain with random-subset ordering and total-variation (TV) regularization. Additionally, we build a time-division-based multicolor light-field microscopy and conduct the three-dimensional (3D) imaging of the heart beating in zebrafish larva at over 95 Hz with a low light dose. RESULTS We demonstrate that this approach reduces computational resources, brings a tenfold speedup, and achieves a tenfold improvement for the noise robustness in terms of SSIM over the state-of-the-art approach. CONCLUSIONS We proposed a phase-space deconvolution algorithm for 3D reconstructions in fluorescence imaging. Compared with the state-of-the-art method, we show significant improvement in both computational effectiveness and noise robustness; we further demonstrated practical application on zebrafish larva with low exposure and low light dose.
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Affiliation(s)
- Tianyi Zhu
- Tsinghua University, Tsinghua-Berkeley Shenzhen Institute, Beijing, China
| | - Yuduo Guo
- Tsinghua University, Tsinghua-Berkeley Shenzhen Institute, Beijing, China
| | - Yi Zhang
- Tsinghua University, Department of Automation, Beijing, China
| | - Zhi Lu
- Tsinghua University, Department of Automation, Beijing, China
| | - Xing Lin
- Tsinghua University, Department of Automation, Beijing, China
| | - Lu Fang
- Tsinghua University, Department of Electronic Engineering, Beijing, China
| | - Jiamin Wu
- Tsinghua University, Department of Automation, Beijing, China
| | - Qionghai Dai
- Tsinghua University, Department of Automation, Beijing, China
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Lu Z, Cai Y, Nie Y, Yang Y, Wu J, Dai Q. A practical guide to scanning light-field microscopy with digital adaptive optics. Nat Protoc 2022; 17:1953-1979. [DOI: 10.1038/s41596-022-00703-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/24/2022] [Indexed: 11/09/2022]
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