Literature DB >> 28864356

Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring.

Yichen Wu1, Aydogan Ozcan2.   

Abstract

Optical compound microscope has been a major tool in biomedical imaging for centuries. Its performance relies on relatively complicated, bulky and expensive lenses and alignment mechanics. In contrast, the lensless microscope digitally reconstructs microscopic images of specimens without using any lenses, as a result of which it can be made much smaller, lighter and lower-cost. Furthermore, the limited space-bandwidth product of objective lenses in a conventional microscope can be significantly surpassed by a lensless microscope. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable and cost-effective devices to potentially address various point-of-care, global-health and telemedicine related challenges. In this review, we discuss the operation principles and the methods behind lensless digital holographic on-chip microscopy. We also go over various applications that are enabled by cost-effective and compact implementations of lensless microscopy, including some recent work on air quality monitoring, which utilized machine learning for high-throughput and accurate quantification of particulate matter in air. Finally, we conclude with a brief future outlook of this computational imaging technology.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28864356     DOI: 10.1016/j.ymeth.2017.08.013

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  16 in total

1.  Lens-Free Imaging as a Sensor for Dynamic Cell Viability Detection Using the Neutral Red Uptake Assay.

Authors:  Brian J Nablo; Jung-Joon Ahn; Kiran Bhadriraju; Jong Muk Lee; Darwin R Reyes
Journal:  ACS Appl Bio Mater       Date:  2020

2.  Design, Development, and Performance Comparison of Wide Field Lensless and Lens-Based Optical Systems for Point-of-Care Biological Applications.

Authors:  Robert D Fennell; Mazhar Sher; Waseem Asghar
Journal:  Opt Lasers Eng       Date:  2020-08-16       Impact factor: 4.836

3.  Holographic optical field recovery using a regularized untrained deep decoder network.

Authors:  Farhad Niknam; Hamed Qazvini; Hamid Latifi
Journal:  Sci Rep       Date:  2021-05-25       Impact factor: 4.379

4.  Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram.

Authors:  Yichen Wu; Yilin Luo; Gunvant Chaudhari; Yair Rivenson; Ayfer Calis; Kevin de Haan; Aydogan Ozcan
Journal:  Light Sci Appl       Date:  2019-03-06       Impact factor: 17.782

5.  Continuous Live-Cell Culture Imaging and Single-Cell Tracking by Computational Lensfree LED Microscopy.

Authors:  Gregor Scholz; Shinta Mariana; Agus Budi Dharmawan; Iqbal Syamsu; Philipp Hörmann; Carsten Reuse; Jana Hartmann; Karsten Hiller; Joan Daniel Prades; Hutomo Suryo Wasisto; Andreas Waag
Journal:  Sensors (Basel)       Date:  2019-03-11       Impact factor: 3.576

6.  Deep learning in holography and coherent imaging.

Authors:  Yair Rivenson; Yichen Wu; Aydogan Ozcan
Journal:  Light Sci Appl       Date:  2019-09-11       Impact factor: 17.782

7.  Pursuing the Diffraction Limit with Nano-LED Scanning Transmission Optical Microscopy.

Authors:  Sergio Moreno; Joan Canals; Victor Moro; Nil Franch; Anna Vilà; Albert Romano-Rodriguez; Joan Daniel Prades; Daria D Bezshlyakh; Andreas Waag; Katarzyna Kluczyk-Korch; Matthias Auf der Maur; Aldo Di Carlo; Sigurd Krieger; Silvana Geleff; Angel Diéguez
Journal:  Sensors (Basel)       Date:  2021-05-11       Impact factor: 3.576

8.  Computational Image Analysis of Guided Acoustic Waves Enables Rheological Assessment of Sub-nanoliter Volumes.

Authors:  Muhammad Arslan Khalid; Aniruddha Ray; Steve Cohen; Manlio Tassieri; Andriejus Demčenko; Derek Tseng; Julien Reboud; Aydogan Ozcan; Jonathan M Cooper
Journal:  ACS Nano       Date:  2019-09-19       Impact factor: 15.881

9.  Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry.

Authors:  Alessio Lugnan; Emmanuel Gooskens; Jeremy Vatin; Joni Dambre; Peter Bienstman
Journal:  Sci Rep       Date:  2020-11-26       Impact factor: 4.379

10.  Virtual organelle self-coding for fluorescence imaging via adversarial learning.

Authors:  Thanh Nguyen; Vy Bui; Anh Thai; Van Lam; Christopher Raub; Lin-Ching Chang; Georges Nehmetallah
Journal:  J Biomed Opt       Date:  2020-09       Impact factor: 3.170

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