Issue 19, 2023

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Abstract

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with “context-aware” matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is “backwards-compatible” with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.

Graphical abstract: Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Supplementary files

Article information

Article type
Paper
Submitted
17 jún 2023
Accepted
20 aug 2023
First published
22 aug 2023

Analyst, 2023,148, 4710-4720

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Z. Jiang, X. Wang, K. Chu and Z. J. Smith, Analyst, 2023, 148, 4710 DOI: 10.1039/D3AN00997A

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