1
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Wang Y, Zhou S, Quan Y, Liu Y, Zhou B, Chen X, Ma Z, Zhou Y. Label-free spatiotemporal decoding of single-cell fate via acoustic driven 3D tomography. Mater Today Bio 2024; 28:101201. [PMID: 39221213 PMCID: PMC11364901 DOI: 10.1016/j.mtbio.2024.101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/08/2024] [Accepted: 08/11/2024] [Indexed: 09/04/2024] Open
Abstract
Label-free three-dimensional imaging plays a crucial role in unraveling the complexities of cellular functions and interactions in biomedical research. Conventional single-cell optical tomography techniques offer affordability and the convenience of bypassing laborious cell labelling protocols. However, these methods are encumbered by restricted illumination scanning ranges on abaxial plane, resulting in the loss of intricate cellular imaging details. The ability to fully control cellular rotation across all angles has emerged as an optimal solution for capturing comprehensive structural details of cells. Here, we introduce a label-free, cost-effective, and readily fabricated contactless acoustic-induced vibration system, specifically designed to enable multi-degree-of-freedom rotation of cells, ultimately attaining stable in-situ rotation. Furthermore, by integrating this system with advanced deep learning technologies, we perform 3D reconstruction and morphological analysis on diverse cell types, thus validating groups of high-precision cell identification. Notably, long-term observation of cells reveals distinct features associated with drug-induced apoptosis in both cancerous and normal cells populations. This methodology, based on deep learning-enabled cell 3D reconstruction, charts a novel trajectory for groups of real-time cellular visualization, offering promising advancements in the realms of drug screening and post-single-cell analysis, thereby addressing potential clinical requisites.
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Affiliation(s)
- Yuxin Wang
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Shizheng Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Yue Quan
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Yu Liu
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Bingpu Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Xiuping Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Zhichao Ma
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai, 200240, China
| | - Yinning Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
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2
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Liu Y, Xiao W, Xiao X, Wang H, Peng R, Feng Y, Zhao Q, Pan F. Dynamic tracking of onion-like carbon nanoparticles in cancer cells using limited-angle holographic tomography with self-supervised learning. BIOMEDICAL OPTICS EXPRESS 2024; 15:3076-3091. [PMID: 38855692 PMCID: PMC11161346 DOI: 10.1364/boe.522563] [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: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 06/11/2024]
Abstract
This research presents a novel approach for the dynamic monitoring of onion-like carbon nanoparticles inside colorectal cancer cells. Onion-like carbon nanoparticles are widely used in photothermal cancer therapy, and precise 3D tracking of their distribution is crucial. We proposed a limited-angle digital holographic tomography technique with unsupervised learning to achieve rapid and accurate monitoring. A key innovation is our internal learning neural network. This network addresses the information limitations of limited-angle measurements by directly mapping coordinates to measured data and reconstructing phase information at unmeasured angles without external training data. We validated the network using standard SiO2 microspheres. Subsequently, we reconstructed the 3D refractive index of onion-like carbon nanoparticles within cancer cells at various time points. Morphological parameters of the nanoparticles were quantitatively analyzed to understand their temporal evolution, offering initial insights into the underlying mechanisms. This methodology provides a new perspective for efficiently tracking nanoparticles within cancer cells.
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Affiliation(s)
- Yakun Liu
- Key Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Wen Xiao
- Key Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Xi Xiao
- Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
- Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Ran Peng
- Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
| | - Yuchen Feng
- Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Qi Zhao
- Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Feng Pan
- Key Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
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3
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Yang Z, Zhang L, Liu T, Wang H, Tang Z, Zhao H, Yuan L, Zhang Z, Liu X. Alternating projection combined with fast gradient projection (FGP-AP) method for intensity-only measurement optical diffraction tomography in LED array microscopy. BIOMEDICAL OPTICS EXPRESS 2024; 15:2524-2542. [PMID: 38633101 PMCID: PMC11019679 DOI: 10.1364/boe.518955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/19/2024]
Abstract
Optical diffraction tomography (ODT) is a powerful label-free measurement tool that can quantitatively image the three-dimensional (3D) refractive index (RI) distribution of samples. However, the inherent "missing cone problem," limited illumination angles, and dependence on intensity-only measurements in a simplified imaging setup can all lead to insufficient information mapping in the Fourier domain, affecting 3D reconstruction results. In this paper, we propose the alternating projection combined with the fast gradient projection (FGP-AP) method to compensate for the above problem, which effectively reconstructs the 3D RI distribution of samples using intensity-only images captured from LED array microscopy. The FGP-AP method employs the alternating projection (AP) algorithm for gradient descent and the fast gradient projection (FGP) algorithm for regularization constraints. This approach is equivalent to incorporating prior knowledge of sample non-negativity and smoothness into the 3D reconstruction process. Simulations demonstrate that the FGP-AP method improves reconstruction quality compared to the original AP method, particularly in the presence of noise. Experimental results, obtained from mouse kidney cells and label-free blood cells, further affirm the superior 3D imaging efficacy of the FGP-AP method.
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Affiliation(s)
- Zewen Yang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Lu Zhang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- School of Instrument Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Tong Liu
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Huijun Wang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Zhiyuan Tang
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
| | - Hong Zhao
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- School of Instrument Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Li Yuan
- First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shannxi, 710049, China
| | - Zhenxi Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Xiaolong Liu
- Mengchao Hepatobiliary Hospital of Fujian Medical University, The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Provincey, Fuzhou 350025, China
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4
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Verrier N, Debailleul M, Haeberlé O. Recent Advances and Current Trends in Transmission Tomographic Diffraction Microscopy. SENSORS (BASEL, SWITZERLAND) 2024; 24:1594. [PMID: 38475130 DOI: 10.3390/s24051594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Optical microscopy techniques are among the most used methods in biomedical sample characterization. In their more advanced realization, optical microscopes demonstrate resolution down to the nanometric scale. These methods rely on the use of fluorescent sample labeling in order to break the diffraction limit. However, fluorescent molecules' phototoxicity or photobleaching is not always compatible with the investigated samples. To overcome this limitation, quantitative phase imaging techniques have been proposed. Among these, holographic imaging has demonstrated its ability to image living microscopic samples without staining. However, for a 3D assessment of samples, tomographic acquisitions are needed. Tomographic Diffraction Microscopy (TDM) combines holographic acquisitions with tomographic reconstructions. Relying on a 3D synthetic aperture process, TDM allows for 3D quantitative measurements of the complex refractive index of the investigated sample. Since its initial proposition by Emil Wolf in 1969, the concept of TDM has found a lot of applications and has become one of the hot topics in biomedical imaging. This review focuses on recent achievements in TDM development. Current trends and perspectives of the technique are also discussed.
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Affiliation(s)
- Nicolas Verrier
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
| | - Matthieu Debailleul
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
| | - Olivier Haeberlé
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
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5
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Sun J, Yang B, Koukourakis N, Guck J, Czarske JW. AI-driven projection tomography with multicore fibre-optic cell rotation. Nat Commun 2024; 15:147. [PMID: 38167247 PMCID: PMC10762230 DOI: 10.1038/s41467-023-44280-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.
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Affiliation(s)
- Jiawei Sun
- Shanghai Artificial Intelligence Laboratory, Longwen Road 129, Xuhui District, 200232, Shanghai, China.
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany.
| | - Bin Yang
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany
| | - Nektarios Koukourakis
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany
| | - Jochen Guck
- Max Planck Institute for the Science of Light & Max Planck-Zentrum für Physik und Medizin, 91058, Erlangen, Germany
| | - Juergen W Czarske
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany.
- Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany.
- Institute of Applied Physics, TU Dresden, Dresden, Germany.
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6
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Wang K, Song L, Wang C, Ren Z, Zhao G, Dou J, Di J, Barbastathis G, Zhou R, Zhao J, Lam EY. On the use of deep learning for phase recovery. LIGHT, SCIENCE & APPLICATIONS 2024; 13:4. [PMID: 38161203 PMCID: PMC10758000 DOI: 10.1038/s41377-023-01340-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 01/03/2024]
Abstract
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
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Affiliation(s)
- Kaiqiang Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Li Song
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chutian Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Zhenbo Ren
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
| | - Guangyuan Zhao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jiazhen Dou
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jianglei Di
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - George Barbastathis
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Renjie Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jianlin Zhao
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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7
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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8
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Bazow B, Phan T, Raub CB, Nehmetallah G. Three-dimensional refractive index estimation based on deep-inverse non-interferometric optical diffraction tomography (ODT-Deep). OPTICS EXPRESS 2023; 31:28382-28399. [PMID: 37710893 DOI: 10.1364/oe.491707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/27/2023] [Indexed: 09/16/2023]
Abstract
Optical diffraction tomography (ODT) solves an inverse scattering problem to obtain label-free, 3D refractive index (RI) estimation of biological specimens. This work demonstrates 3D RI retrieval methods suitable for partially-coherent ODT systems supported by intensity-only measurements consisting of axial and angular illumination scanning. This framework allows for access to 3D quantitative RI contrast using a simplified non-interferometric technique. We consider a traditional iterative tomographic solver based on a multiple in-plane representation of the optical scattering process and gradient descent optimization adapted for focus-scanning systems, as well as an approach that relies solely on 3D convolutional neural networks (CNNs) to invert the scattering process. The approaches are validated using simulations of the 3D scattering potential for weak phase 3D biological samples.
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9
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Ryu D, Bak T, Ahn D, Kang H, Oh S, Min HS, Lee S, Lee J. Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon 2023; 9:e18297. [PMID: 37576294 PMCID: PMC10412892 DOI: 10.1016/j.heliyon.2023.e18297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.
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Affiliation(s)
- Dongmin Ryu
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Taeyoung Bak
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hayoung Kang
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Sanggeun Oh
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | | | - Sumin Lee
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Jimin Lee
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
- Graduate School of Artificial Intelligence (AIGS), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
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10
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Liang F, Zhu J, Chai H, Feng Y, Zhao P, Liu S, Yang Y, Lin L, Cao L, Wang W. Non-Invasive and Minute-Frequency 3D Tomographic Imaging Enabling Long-Term Spatiotemporal Observation of Single Cell Fate. SMALL METHODS 2023:e2201492. [PMID: 36950762 DOI: 10.1002/smtd.202201492] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Non-invasive and rapid imaging technique at subcellular resolution is significantly important for multiple biological applications such as cell fate study. Label-free refractive-index (RI)-based 3D tomographic imaging constitutes an excellent candidate for 3D imaging of cellular structures, but its full potential in long-term spatiotemporal cell fate observation is locked due to the lack of an efficient integrated system. Here, a long-term 3D RI imaging system incorporating a cutting-edge white light diffraction phase microscopy module with spatiotemporal stability, and an acoustofluidic device to roll and culture single cells in a customized live cell culture chamber is reported. Using this system, 3D RI imaging experiments are conducted for 250 cells and demonstrate efficient cell identification with high accuracy. Importantly, long-term and frequency-on-demand 3D RI imaging of K562 and MCF-7 cancer cells reveal different characteristics during normal cell growth, drug-induced cell apoptosis, and necrosis of drug-treated cells. Overall, it is believed that the proposed 3D tomographic imaging technique opens up a new avenue for visualizing intracellular structures and will find many applications such as disease diagnosis and nanomedicine.
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Affiliation(s)
- Fei Liang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Junwen Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Huichao Chai
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yongxiang Feng
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Peng Zhao
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Shaofeng Liu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yuanmu Yang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Linhan Lin
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Liangcai Cao
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Wenhui Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
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11
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Kim G, Ahn D, Kang M, Park J, Ryu D, Jo Y, Song J, Ryu JS, Choi G, Chung HJ, Kim K, Chung DR, Yoo IY, Huh HJ, Min HS, Lee NY, Park Y. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. LIGHT, SCIENCE & APPLICATIONS 2022; 11:190. [PMID: 35739098 PMCID: PMC9226356 DOI: 10.1038/s41377-022-00881-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 05/14/2023]
Abstract
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
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Affiliation(s)
- Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Minhee Kang
- Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Jinho Park
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
- Tomocube Inc., Daejeon, 34109, Republic of Korea
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jea Sung Ryu
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Gunho Choi
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hyun Jung Chung
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Kyuseok Kim
- Department of Emergency Medicine, Bundang CHA Hospital, Seongnam-si, Gyeonggi-Do, 13496, Korea
| | - Doo Ryeon Chung
- Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - In Young Yoo
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
| | - Hee Jae Huh
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | | | - Nam Yong Lee
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea.
- Tomocube Inc., Daejeon, 34109, Republic of Korea.
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12
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Roadmap on Digital Holography-Based Quantitative Phase Imaging. J Imaging 2021; 7:jimaging7120252. [PMID: 34940719 PMCID: PMC8703719 DOI: 10.3390/jimaging7120252] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/11/2021] [Accepted: 11/15/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitative Phase Imaging (QPI) provides unique means for the imaging of biological or technical microstructures, merging beneficial features identified with microscopy, interferometry, holography, and numerical computations. This roadmap article reviews several digital holography-based QPI approaches developed by prominent research groups. It also briefly discusses the present and future perspectives of 2D and 3D QPI research based on digital holographic microscopy, holographic tomography, and their applications.
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13
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Long JM, Chun JY, Gaylord TK. ADMM approach for efficient iterative tomographic deconvolution reconstruction of 3D quantitative phase images. APPLIED OPTICS 2021; 60:8485-8492. [PMID: 34612951 DOI: 10.1364/ao.433999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/20/2021] [Indexed: 06/13/2023]
Abstract
Tomographic deconvolution phase microscopy (TDPM) is a promising approach for 3D quantitative imaging of phase objects such as biological cells and optical fibers. In the present work, the alternating direction method of multipliers (ADMM) is applied to TDPM to shorten its image acquisition and processing times while simultaneously improving its accuracy. ADMM-TDPM is used to optimize the image fidelity by minimizing Gaussian noise and by using total variation regularization with the constraints of nonnegativity and known zeros. ADMM-TDPM can reconstruct phase objects that are shift variant in three spatial dimensions. ADMM-TDPM achieves speedups of 5x in image acquisition time and greater than 10x in image processing time with accompanying higher accuracy compared to TDPM.
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