Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy

Nia C. Jenkins; Katjana Ehrlich; András Kufcsák; Stephanos Yerolatsitis; Susan Fernandes; Irene Young; Katie Hamilton; Harry A. C. Wood; Tom Quinn; Vikki Young; Ahsan R. Akram; James M. Stone; Robert R. Thomson; Keith Finlayson; Kevin Dhaliwal; Sohan Seth

doi:10.1109/TBME.2023.3243866

Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy

Nia C. Jenkins, Katjana Ehrlich, András Kufcsák, Stephanos Yerolatsitis, Susan Fernandes, Irene Young, Katie Hamilton, Harry A. C. Wood, Tom Quinn, Vikki Young, Ahsan R. Akram, James M. Stone, Robert R. Thomson, Keith Finlayson, Kevin Dhaliwal, Sohan Seth^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.

Original language	English
Pages (from-to)	2374-2383
Number of pages	10
Journal	IEEE Transactions on Biomedical Engineering
Volume	70
Issue number	8
Early online date	10 Feb 2023
DOIs	https://doi.org/10.1109/TBME.2023.3243866
Publication status	Published - Aug 2023

Keywords

Biomedical
fluorescence
lung tissue
machine learning
optical fiber
raman spectroscopy
regularization
shifted excitation
smoothness
sparsity

ASJC Scopus subject areas

Biomedical Engineering

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Jenkins, N. C., Ehrlich, K., Kufcsák, A., Yerolatsitis, S., Fernandes, S., Young, I., Hamilton, K., Wood, H. A. C., Quinn, T., Young, V., Akram, A. R., Stone, J. M., Thomson, R. R., Finlayson, K., Dhaliwal, K., & Seth, S. (2023). Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy. IEEE Transactions on Biomedical Engineering, 70(8), 2374-2383. https://doi.org/10.1109/TBME.2023.3243866

@article{a42d8b8b6e8f460c80825900719e6593,

title = "Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy",

abstract = "Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.",

keywords = "Biomedical, fluorescence, lung tissue, machine learning, optical fiber, raman spectroscopy, regularization, shifted excitation, smoothness, sparsity",

author = "Jenkins, {Nia C.} and Katjana Ehrlich and Andr{\'a}s Kufcs{\'a}k and Stephanos Yerolatsitis and Susan Fernandes and Irene Young and Katie Hamilton and Wood, {Harry A. C.} and Tom Quinn and Vikki Young and Akram, {Ahsan R.} and Stone, {James M.} and Thomson, {Robert R.} and Keith Finlayson and Kevin Dhaliwal and Sohan Seth",

note = "Funding Information: The work of Nia C. Jenkins was supported by the University of Edinburgh. The work of James M. Stone, Stephanos Yerolatsitis, and Harry A. C. Wood was supported by EPSRC fellowship under Grant EP/S001123/1. The work of Susan Fernandes was supported by MRC fellowship under Grant MR/R017794/1. The work of Ahsan R. Akram was supported by the CRUK Clinician Scientist Fellowship under Grant A24867. The authors thank the BioResource for access to tissue (NHS Lothian BioResource, Scotland Research Ethics Service, reference 15/ES/0094). Publisher Copyright: {\textcopyright} 2023 IEEE.",

year = "2023",

month = aug,

doi = "10.1109/TBME.2023.3243866",

language = "English",

volume = "70",

pages = "2374--2383",

journal = "IEEE Transactions on Biomedical Engineering",

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Jenkins, NC, Ehrlich, K, Kufcsák, A, Yerolatsitis, S, Fernandes, S, Young, I, Hamilton, K, Wood, HAC, Quinn, T, Young, V, Akram, AR, Stone, JM, Thomson, RR, Finlayson, K, Dhaliwal, K & Seth, S 2023, 'Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy', IEEE Transactions on Biomedical Engineering, vol. 70, no. 8, pp. 2374-2383. https://doi.org/10.1109/TBME.2023.3243866

TY - JOUR

T1 - Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy

AU - Jenkins, Nia C.

AU - Ehrlich, Katjana

AU - Kufcsák, András

AU - Yerolatsitis, Stephanos

AU - Fernandes, Susan

AU - Young, Irene

AU - Hamilton, Katie

AU - Wood, Harry A. C.

AU - Quinn, Tom

AU - Young, Vikki

AU - Akram, Ahsan R.

AU - Stone, James M.

AU - Thomson, Robert R.

AU - Finlayson, Keith

AU - Dhaliwal, Kevin

AU - Seth, Sohan

N1 - Funding Information: The work of Nia C. Jenkins was supported by the University of Edinburgh. The work of James M. Stone, Stephanos Yerolatsitis, and Harry A. C. Wood was supported by EPSRC fellowship under Grant EP/S001123/1. The work of Susan Fernandes was supported by MRC fellowship under Grant MR/R017794/1. The work of Ahsan R. Akram was supported by the CRUK Clinician Scientist Fellowship under Grant A24867. The authors thank the BioResource for access to tissue (NHS Lothian BioResource, Scotland Research Ethics Service, reference 15/ES/0094). Publisher Copyright: © 2023 IEEE.

PY - 2023/8

Y1 - 2023/8

N2 - Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.

AB - Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.

KW - Biomedical

KW - fluorescence

KW - lung tissue

KW - machine learning

KW - optical fiber

KW - raman spectroscopy

KW - regularization

KW - shifted excitation

KW - smoothness

KW - sparsity

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DO - 10.1109/TBME.2023.3243866

M3 - Article

C2 - 37022914

AN - SCOPUS:85149391627

SN - 0018-9294

VL - 70

SP - 2374

EP - 2383

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

IS - 8

ER -

Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy

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