Literature DB >> 24322867

Optically amplified detection for biomedical sensing and imaging.

Ata Mahjoubfar, Keisuke Goda, Gary Betts, Bahram Jalali.   

Abstract

Optical sensing and imaging methods for biomedical applications, such as spectroscopy and laser-scanning fluorescence microscopy, are incapable of performing sensitive detection at high scan rates due to the fundamental trade-off between sensitivity and speed. This is because fewer photons are detected during short integration times and hence the signal falls below the detector noise. Optical postamplification can, however, overcome this challenge by amplifying the collected optical signal after collection and before photodetection. Here we present a theoretical analysis of the sensitivity of high-speed biomedical sensing and imaging systems enhanced by optical postamplifiers. As a case study, we focus on Raman amplifiers because they produce gain at any wavelength within the gain medium's transparency window and are hence suitable for biomedical applications. Our analytical model shows that when limited by detector noise, such optically postamplified systems can achieve a sensitivity improvement of up to 20 dB in the visible to near-infrared spectral range without sacrificing speed. This analysis is expected to be valuable for design of fast real-time biomedical sensing and imaging systems.

Mesh:

Year:  2013        PMID: 24322867     DOI: 10.1364/JOSAA.30.002124

Source DB:  PubMed          Journal:  J Opt Soc Am A Opt Image Sci Vis        ISSN: 1084-7529            Impact factor:   2.129


  4 in total

1.  Optical data compression in time stretch imaging.

Authors:  Claire Lifan Chen; Ata Mahjoubfar; Bahram Jalali
Journal:  PLoS One       Date:  2015-04-23       Impact factor: 3.240

2.  Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.

Authors:  Yueqin Li; Ata Mahjoubfar; Claire Lifan Chen; Kayvan Reza Niazi; Li Pei; Bahram Jalali
Journal:  Sci Rep       Date:  2019-07-31       Impact factor: 4.379

3.  Multi-pronged approach to human mesenchymal stromal cells senescence quantification with a focus on label-free methods.

Authors:  Weichao Zhai; Jerome Tan; Tobias Russell; Sixun Chen; Dennis McGonagle; May Win Naing; Derrick Yong; Elena Jones
Journal:  Sci Rep       Date:  2021-01-13       Impact factor: 4.379

4.  Deep Learning in Label-free Cell Classification.

Authors:  Claire Lifan Chen; Ata Mahjoubfar; Li-Chia Tai; Ian K Blaby; Allen Huang; Kayvan Reza Niazi; Bahram Jalali
Journal:  Sci Rep       Date:  2016-03-15       Impact factor: 4.379

  4 in total

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