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
28 ذو القعدة 1444
Accepted
04 صفر 1445
First published
06 صفر 1445

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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