Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science

Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis....
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Gespeichert in:

Autor*in:	Lin, Haonan [verfasserIn] Cheng, Ji-Xin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Coherent anti-Stokes Raman scattering Stimulated Raman scattering Computational imaging Hyperspectral imaging Deep learning

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: eLight - [Singapore] : Springer Singapore, 2021, 3(2023), 1 vom: 20. März
Übergeordnetes Werk:	volume:3 ; year:2023 ; number:1 ; day:20 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s43593-022-00038-8

Katalog-ID:	SPR049747118

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spelling	10.1186/s43593-022-00038-8 doi (DE-627)SPR049747118 (SPR)s43593-022-00038-8-e DE-627 ger DE-627 rakwb eng Lin, Haonan verfasserin aut Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. Coherent anti-Stokes Raman scattering (dpeaa)DE-He213 Stimulated Raman scattering (dpeaa)DE-He213 Computational imaging (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cheng, Ji-Xin (orcid)0000-0002-5607-6683 aut Enthalten in eLight [Singapore] : Springer Singapore, 2021 3(2023), 1 vom: 20. März (DE-627)176201291X (DE-600)3075589-X 2662-8643 nnns volume:3 year:2023 number:1 day:20 month:03 https://dx.doi.org/10.1186/s43593-022-00038-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 20 03
allfields_unstemmed	10.1186/s43593-022-00038-8 doi (DE-627)SPR049747118 (SPR)s43593-022-00038-8-e DE-627 ger DE-627 rakwb eng Lin, Haonan verfasserin aut Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. Coherent anti-Stokes Raman scattering (dpeaa)DE-He213 Stimulated Raman scattering (dpeaa)DE-He213 Computational imaging (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cheng, Ji-Xin (orcid)0000-0002-5607-6683 aut Enthalten in eLight [Singapore] : Springer Singapore, 2021 3(2023), 1 vom: 20. März (DE-627)176201291X (DE-600)3075589-X 2662-8643 nnns volume:3 year:2023 number:1 day:20 month:03 https://dx.doi.org/10.1186/s43593-022-00038-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 20 03
allfieldsGer	10.1186/s43593-022-00038-8 doi (DE-627)SPR049747118 (SPR)s43593-022-00038-8-e DE-627 ger DE-627 rakwb eng Lin, Haonan verfasserin aut Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. Coherent anti-Stokes Raman scattering (dpeaa)DE-He213 Stimulated Raman scattering (dpeaa)DE-He213 Computational imaging (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cheng, Ji-Xin (orcid)0000-0002-5607-6683 aut Enthalten in eLight [Singapore] : Springer Singapore, 2021 3(2023), 1 vom: 20. März (DE-627)176201291X (DE-600)3075589-X 2662-8643 nnns volume:3 year:2023 number:1 day:20 month:03 https://dx.doi.org/10.1186/s43593-022-00038-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 20 03
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topic_facet	Coherent anti-Stokes Raman scattering Stimulated Raman scattering Computational imaging Hyperspectral imaging Deep learning
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authorswithroles_txt_mv	Lin, Haonan @@aut@@ Cheng, Ji-Xin @@aut@@
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author	Lin, Haonan
spellingShingle	Lin, Haonan misc Coherent anti-Stokes Raman scattering misc Stimulated Raman scattering misc Computational imaging misc Hyperspectral imaging misc Deep learning Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
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topic_title	Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science Coherent anti-Stokes Raman scattering (dpeaa)DE-He213 Stimulated Raman scattering (dpeaa)DE-He213 Computational imaging (dpeaa)DE-He213 Hyperspectral imaging (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213
topic	misc Coherent anti-Stokes Raman scattering misc Stimulated Raman scattering misc Computational imaging misc Hyperspectral imaging misc Deep learning
topic_unstemmed	misc Coherent anti-Stokes Raman scattering misc Stimulated Raman scattering misc Computational imaging misc Hyperspectral imaging misc Deep learning
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title	Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
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title_full	Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
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title_sort	computational coherent raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
title_auth	Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
abstract	Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. © The Author(s) 2023
abstractGer	Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. © The Author(s) 2023
abstract_unstemmed	Abstract Coherent Raman scattering (CRS) microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular vibrations. To date, endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical diagnosis. Nevertheless, the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system, which trades off speed, signal fidelity and spectral bandwidth. The synergistic combination of instrumentation and computational approaches offers a way to break the trade-off. In this review, we first introduce coherent Raman scattering and recent instrumentation developments, then discuss current computational CRS imaging methods, including compressive micro-spectroscopy, computational volumetric imaging, as well as machine learning algorithms that improve system performance and decipher chemical information. We foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy. © The Author(s) 2023
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title_short	Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
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Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science

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