Field guide for building a broadband CARS system for biomedical applications

Muddiman, Ryan; O’Dwyer, Kevin; Tang, Zhengqiang; Hennelly, Bryan M.

doi:10.1117/12.2601223

Cited by 3 publications

(4 citation statements)

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“…The details of the BCARS system are described elsewhere. 4 We obtained the S + and S − measurements sequentially using two different polarizing analyzer orientations. We also recorded the spontaneous Raman spectrum of both materials for a comparison of the Raman shift.…”

Section: Resultsmentioning

confidence: 99%

Spectral interferometric polarization-CARS for hyperspectral imaging

Muddiman,

Hennelly

2023

Optics in Health Care and Biomedical Optics XIII

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The third-order nonlinear nature of Broadband CARS means it has the inherent capability to produce label-free images using vibrational information as contrast. The speed of the technique is several orders of magnitude greater than spontaneous Raman spectroscopy. This has implications for enabling diagnostics in areas such as histology, immunology and cytology. The major drawback in BCARS currently preventing these applications is mainly the nonresonant background, present due to degenerate four-wave mixing. This background can be removed using the tensorial properties of the electronic susceptibility. This technique is known as spectral interferometric polarization CARS (SIP-CARS). We show an implementation of SIP-CARS on highly resonant polymer beads for spectroscopic imaging using two sequential BCARS scans to probe different components of the susceptibility tensor.

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Section: Resultsmentioning

confidence: 99%

Spectral interferometric polarization-CARS for hyperspectral imaging

Muddiman,

Hennelly

2023

Optics in Health Care and Biomedical Optics XIII

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“…The details of this approach are described in detail in our previous paper. 21 Since hyperspectral imaging is an unsupervised process (unless ground-truth data is available through other means), FC-NMF was applied to discern cell features through dimensionality reduction of the data. FC-NMF was able to provide meaningful unmixed spectra of the cytoplasm (green), nucleus (blue) and cell memberane (red) as shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we present a concise overview of our architectural framework, with a more comprehensive description available in our original paper. 21 Our deep-learning network belongs to the category of deep autoencoders. A deep autoencoder (AE), is a neural network designed to efficiently encode input data into a lower-dimensional latent space representation.…”

Section: Nrb Removal Using a Deep Autoencodermentioning

confidence: 99%

“…We describe in detail the NRB removal process in our paper. 21 After NRB removal, we implement spatio-spectral denosing using the spectral total variation (STV) algorithm. 25 STV uses the spatial and spectral information in a hyperspectral image to improve the SNR by estimating the 2D image noise per spectral datum and applies regularization according to the noise.…”

Section: Post-processing Of Bcars Imagesmentioning

confidence: 99%

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Deep-learning autoencoders for unsupervised BCARS chemical imaging

Muddiman,

Hennelly

2023

Optics in Health Care and Biomedical Optics XIII

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The application of Broadband CARS to cell imaging studies has thus far been limited to those where highcontrast features are present, such as lipids and exogenously introduced tags. This is due to the inherent low SNR obtained in BCARS from the low density of oscillators in single cells coupled with the non-resonant background present in all media which distorts the measured signal. In this paper, we show that an autoencoder which we named VECTOR2, trained on simulated spectra, can accurately perform NRB removal of recorded BCARS images of unstained biological specimen. This allows cell imaging comparable in time to spontaneous Raman imaging with high bandwidth and resolution. The introduction of standard baseline flattening prior to NRB removal preserves the image structure while removing artefacts from raster scanning and optical noise. This results in a hyperspectral image of the NRB-free BCARS signal which is linear in the sample concentration and has a spectrum that is very similar to the spontaneous Raman spectrum.

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Broadband coherent anti-Stokes Raman spectroscopy for small scale microplastic detection

Baryalay,

Byrne,

McNamara

et al. 2024

Nonlinear Optics and Its Applications 2024

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Microplastics contamination in water sources presents a pressing concern for environmental and public health, necessitating accurate detection and quantification methods. We investigate the application of broadband Coherent anti-Stokes Raman Spectroscopy (BCARS) as an innovative, rapid, and label-free spectroscopy method for the detection of microplastics in drinking water. Current methods for detecting microplastics, such as visual inspection, FTIR and spontaneous Raman spectroscopy, and gas chromatography, have limitations in terms of sensitivity, speed, specificity, or destructive analysis. BCARS, however, offers a non-destructive approach with the capability to identify particles smaller than a micron and to discern different types of plastics through chemical analysis. BCARS operates at a significantly faster rate than spontaneous Raman spectroscopy, reducing acquisition time from seconds to milliseconds. BCARS utilizes a dual excitation technique to simultaneously probe both the fingerprint and C-H band regions of the Raman spectrum and allows for the identification of different polymers in a sample, as demonstrated in this study with a mixture of Polystyrene and Poly(methyl methacrylate) (PMMA) micro-beads. Our results highlight the ability of B-CARS to distinguish between different types of polymers in a sample, using resonant peaks at specific wave-numbers to generate a false-color image for easy identification.

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Field guide for building a broadband CARS system for biomedical applications

Cited by 3 publications

References 0 publications

Spectral interferometric polarization-CARS for hyperspectral imaging

Spectral interferometric polarization-CARS for hyperspectral imaging

Deep-learning autoencoders for unsupervised BCARS chemical imaging

Broadband coherent anti-Stokes Raman spectroscopy for small scale microplastic detection

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