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

Haonan Lin; Ji-Xin Cheng

doi:10.1186/s43593-022-00038-8

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

eLight Vol. 3, Issue 1, (2023)

REVIEW | Updated：2024-09-25

- Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science
- Affiliations：
  
  Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Photonics Center, Boston University, Boston, MA 02215, USA
- Author bio：
  
  *jxcheng@bu.edu
- Funds：
- DOI：10.1186/s43593-022-00038-8
  CLC：
- Published：2023，
  
  Published Online：20 March 2023，
  
  Received：17 September 2022，
  
  Revised：08 November 2022，
  
  Accepted：01 December 2022
- Accepted：
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Haonan Lin, Ji-Xin Cheng. Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science. [J]. eLight, 2023,3(1).
DOI：

Haonan Lin, Ji-Xin Cheng. Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science. [J]. eLight, 2023,3(1). DOI： 10.1186/s43593-022-00038-8.

摘要

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.

关键词

Keywords

Coherent anti-Stokes Raman scatteringStimulated Raman scatteringComputational imagingHyperspectral imagingDeep learning

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