Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging

Published

June 9, 2021

Author(s)

Charles Camp, John S. Bender, Young Lee

Abstract

We present a new collection of processing techniques, collectively "factorized Kramers-Kroenig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale- error correction in coherent anti-Stokes Raman scattering (CARS) hyperspectral imaging and spectroscopy. These new methods are orders-or-magnitude faster than conventional methods and capable of real-time performance, owing to the unique core concept: performing all processing on a small basis vector set and using matrix/vector multiplication afterwards for direct and fast transformation of the entire dataset. Experimentally, we demonstrate that a 703,026 spectra image of chicken cartilage can be processed in 70 s (0.1 ms / spectrum), which is > 70 times faster than with the conventional workflow (7.0 ms / spectrum). Additionally, we discuss that this method may be used in a machine learning (ML) fashion in which the transformed basis vector sets may be re-used with new data. Using this ML paradigm, the same tissue image was processed in 40 s, which is a speed-up of > 150 times.

Citation

Optics Express

Volume

28

Issue

14

Pub Type

Journals

Download Paper

https://doi.org/10.1364/OE.397606

Local Download

Keywords

BCARS, CARS, Raman, spectroscopy, machine learning, hyperspectral imaging, chemical imaging

Citation

Camp, C. , Bender, J. and Lee, Y. (2021), Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging, Optics Express, [online], https://doi.org/10.1364/OE.397606, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930220 (Accessed October 9, 2025)