Coherent anti-Stokes Raman Scattering Research Articles

Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm.

Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspectral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, ≈4 frames/s, for a pixel resolution of 95 × 95 pixels and a spectral bandwidth no less than 200 cm-1. To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum.

Multivariate curve resolution with autoencoders for CARS microspectroscopy

Coherent anti-Stokes Raman scattering (CARS) microspectroscopy is a powerful tool for label-free cell imaging thanks to its ability to acquire a rich amount of information. An important family of operations applied to such data is multivariate curve resolution (MCR). It aims to find main components of a dataset and compute their spectra and concentrations in each pixel. Recently, autoencoders began to be studied to accomplish MCR with dense and convolutional models. However, many questions, like the results variability or the reconstruction metric, remain open and applications are limited to hyperspectral imaging. In this article, we present a nonlinear convolutional encoder combined with a linear decoder to apply MCR to CARS microspectroscopy. We conclude with a study of the result variability induced by the encoder initialization.

Spectral Unmixing for Label-Free, In-Liquid Characterization of Biomass Microstructure and Biopolymer Content by Coherent Raman Imaging.

Characterization of lignocellulosic biomass microstructure with chemical specificity and under physiological conditions could provide invaluable insights to our understanding of plant tissue development, microstructure, origins of recalcitrance, degradation, and solubilization. However, most methods currently available are either destructive, are not compatible with hosting a physiological environment, or introduces exogenous probes, complicating their use for studying changes in microstructure and mechanisms of plant development, recalcitrance, or degradation in situ. To address these challenges, we here present a multi-modal chemically specific imaging technique based on coherent anti-Stokes Raman scattering (CARS) microspectroscopy with simplex maximization and entropy-based spectral unmixing enabling label-free, chemically specific characterization of plant microstructure in liquid. We describe how spatial drift of samples suspended in liquid can introduce artifacts in spectral unmixing procedures for single-frequency CARS and propose a mitigative strategy toward these effects using simultaneously acquired forward-scattered CARS signals and epi-detected autofluorescence. We further apply the technique for chemical and microstructural characterization of untreated and liquid hot water pretreated rapeseed straw by CARS and show how the framework can be extended for 3D imaging with chemical specificity. Finally, we provide examples of the intricate chemical and microstructural details recovered by this hybrid imaging technique, including discerning between primary and secondary cell walls, localization of aqueous components to cell lumina, and the presence of funnel-type pits in samples ofBrassica napus.

Dissolvable microneedles for transdermal drug delivery showing skin penetration and modified drug release

Topical therapies for chronic skin diseases suffer from a low patient compliance due to the inconvenient treatment regimens of available products. Dissolvable microneedles (MN) with modified release offer an interesting possibility to increase the compliance by acting as a depot in the skin and thereby decreasing the dosing frequency. Furthermore, the bioavailability can be increased significantly by bypassing the barrier of the skin by the direct penetration of the MN into the skin. In this study the depot effect and skin penetration of an innovative dissolvable MN patch was assessed by insertion in ex vivo human skin and in vivo using minipigs. The MN patches are based on biodegradable polymers and the active pharmaceutical ingredients calcipotriol (Calci) and betamethasone-17–21-dipropionate (BDP) used to treat psoriasis. Using computed tomography (CT) and Coherent anti-Stokes Raman scattering (CARS) microscopy it was possible to visualize the skin penetration and follow the morphology of the MN as function of time in the skin. The depot effect was assessed by studying the modified in vitro release in an aqueous buffer and by comparing the drug release of a single application of a patch both ex vivo and in vivo to daily application of a marketed oleogel containing the same active pharmaceutical ingredients. The CT and CARS images showed efficient penetration of the MN patches into the upper dermis and a slow swelling process of the drug containing tip over a period of 8 days. Furthermore, CARS demonstrated that it can be used as a noninvasive technique with potential applicability in clinical settings. The in vitro release studies show a release of 54% over a time period of 30 days. The pharmacological relevance of MNs was confirmed in human skin explants and in vivo after single application and showed a similar response on calcipotriol and BDP mediated signaling events compared to daily application of the active oleogel. Altogether it was demonstrated that the MN can penetrate the skin and have the potential to provide a depot effect.

Nonlinear optical microscopy for skinin vivo: Basics, development and applications

Multi-photon microscopy (MPM) and coherent anti-Stokes Raman scattering (CARS) are two advanced nonlinear optical imaging techniques, which provide complementary information and have great potential in combination for noninvasive in vivo biomedical applications. This paper provides a detailed discussion of the basics, development and applications of these technologies for in vivo skin research, covering the following topics: The principle and advantage of MPM and CARS, instrumentation development for in vivo applications, MPM and CARS of normal skin, application of MPM and CARS in skin cancer and disease diagnosis; application of MPM in skin disease intervention, i.e., imaging guided two-photon photothermolysis.

Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra.

The nonresonant background (NRB) contribution to the coherent anti-Stokes Raman scattering (CARS) signal distorts the spectral line shapes and thus degrades the chemical information. Hence, finding an effective approach for removing NRB and extracting resonant vibrational signals is a challenging task. In this work, a bidirectional LSTM (Bi-LSTM) neural network is explored for the first time to remove the NRB in the CARS spectra automatically, and the results are compared with those of three DL models reported in the literature, namely, convolutional neural network (CNN), long short-term memory (LSTM) neural network, and very deep convolutional autoencoders (VECTOR). The results of the synthetic test data have shown that the Bi-LSTM model accurately extracts the spectral lines throughout the range. In contrast, the other three models' efficiency deteriorated while predicting the peaks on either end of the spectra, which resulted in a 60 times higher mean square error than that of the Bi-LSTM model. The Pearson correlation analysis demonstrated that Bi-LSTM model performance stands out from the rest, where 94% of the test spectra have correlation coefficients of more than 0.99. Finally, these four models were evaluated on four complex experimental CARS spectra, namely, protein, yeast, DMPC, and ADP, where the Bi-LSTM model has shown superior performance, followed by CNN, VECTOR, and LSTM. This comprehensive study provides a giant leap toward simplifying the analysis of complex CARS spectroscopy and microscopy.

Plasma-assisted deflagration to detonation transition in a microchannel with fast-frame imaging and hybrid fs/ps coherent anti-Stokes Raman scattering measurements

This study examines kinetic enhancement by nanosecond dielectric barrier discharge (ns-DBD) plasma on fuel-lean, Φ = 0.7, dimethyl ether (DME), oxygen (O2), and argon (Ar) premixture during deflagration to detonation transition (DDT) experiments in a microchannel. Nonequilibrium plasma produces active species and radicals as well as fast and slow heating of a mixture to promote ignition due to energetic electrons, ions, and electronic and vibrational excitations. Experiments are conducted to examine the influence of the plasma discharge on the premixture and on the resultant deflagration to detonation transition (DDT) onset time and distance through the use of high-speed imaging and one-dimensional, two-beam, femtosecond/picosecond, coherent anti-Stokes Raman scattering (CARS). A high-speed camera is used to trace the time histories of flame front position and velocity and to identify the dynamics and onset of DDT. The results show that plasma discharge can nonlinearly affect the onset time and distance of DDT. It is shown that a small number of plasma discharge pulses prior to ignition result in reduced DDT onset time and distance by 60% and 40%, respectively, when compared to the results without pre-excitation by ns discharges. The results also show that an increase of number of the plasma discharge pulses results in an extended DDT onset time and distance of 224% and 94%, respectively. Time history of the deflagration wave speed of DME and the analysis of ignition timescale under the choking condition of the burned gas of the deflagration wave suggest low temperature ignition may play a role for DME near the isobaric choking condition of the burned gas and the DDT. Plasma-induced low temperature oxidation of the reactive mixture is assessed via the CO2 to O2 ratio as measured through fs/ps CARS during the gas excitation in discharges. CARS measurements also confirm negligible vibrational and rotational heating of the gas by discharge. The present experiments demonstrate the ability of nonequilibrium plasma to alter the chemistry of DME/O2/Ar premixtures in order to control DDT for applications in advanced propulsion engines.

Broadband CARS high-throughput single-cell imaging

Broadband Coherent anti-Stokes Raman Scattering (BCARS) enables the whole vibrational spectrum of cytologically prepared samples to be obtained using a hyperspectral raster scan approach. This technique has the potential to enable high-throughput automated detection of cell abnormalities. Images are distorted by the non-resonant background which requires a treatment for proper analysis. Using statistical denoising and phase retrieval returns Raman spectra similar to that of a spontaneous Raman measurement. Here, we present our work using this method for single-cell imaging of PEO1 ovarian adenocarcinoma cells prepared with the ThinPrep processor which enables label-free Raman cytology.

Raman signal extraction from BCARS intensity measurements using deep learning with a prior excitation profile

Broadband Coherent anti-Stokes Raman Scattering (BCARS) microscopy is a useful technique for chemical analysis and allows the full vibrational fingerprint spectrum of a specimen to be obtained in millisec-onds. A major drawback to this technique is the presence of the non-resonant background response producing interference which prevents classical spectral analysis of the sample. Using a convolutional autoencoder and measurements of the laser characteristics, we have shown that it is possible to remove this background with-out requiring supervision, as is typically required for conventional removal methods. This approach therefore simplifies the analysis of hyperspectral images obtained with BCARS.

Planar Laser Induced Fluorescence of OH for Thermometry in a Flow Field Based on Two Temperature Point Calibration Method

In view of the uncertainty in the calibration process of two-color plane laser-induced fluorescence (PLIF) temperature measurement, a new calibration method is proposed, in which the influence of fluorescence yield is considered. The calibration process was carried out at high and low temperature region, respectively. Then, the bias of thermometry results origin from quenching is restrained. This new calibration method is validated in a jet flame with temperature range of 1300–1800 K. Here, the temperature results from Coherent Anti-Stokes Raman scattering (CARS), single-point calibrated PLIF, and two-point calibrated PLIF are all acquired with the maximum standard errors of 13 K, 36 K, and 37 K, respectively. The temperature deviation between the average results from PLIF and Coherent Anti-Stokes Raman scattering (CARS) is 120 K and 10 K, when the two-point and one-point calibration methods are used. Therefore, the two-point calibrated PLIF is preferred in the combustion field, especially with a large temperature range and strong quenching coefficient.

Frequency conversion in pressurized hydrogen.

State-preserving frequency conversion in the optical domain is a necessary component in many configurations of quantum information processing and communication. Thus far, nonlinear crystals are used for this purpose. Here, we report on an approach based on coherent anti-Stokes Raman scattering (CARS) in a dense molecular hydrogen gas. This four-wave mixing process sidesteps the limitations imposed by crystal properties, it is intrinsically broadband and does not generate an undesired background. We demonstrate this method by converting photons from 434 nm to 370 nm and show that their polarization is preserved.

Hybrid time-frequency domain dual-probe coherent anti-Stokes Raman scattering for simultaneous temperature and pressure measurements in compressible flows via spectral fitting.

We demonstrate a hybrid time-frequency spectroscopic method for simultaneous temperature/pressure measurements in nonreacting compressible flows with known gas composition. Hybrid femtosecond-picosecond, pure-rotational coherent anti-Stokes Raman scattering (CARS), with two independent, time-delayed probe pulses, is deployed for single-laser-shot measurements of temperature and pressure profiles along an ∼5-mm line. The theory of dual-probe CARS is presented, along with a discussion of the iterative fitting of experimental spectra. Temperature is obtained from spectra acquired with an early, near-collision-free probe time delay (τ 1=0p s) and pressure from spectra obtained at probe delays of τ 2=150-1000p s, where collisions significantly impact the spectral profile. Unique solutions for temperature and pressure are obtained by iteratively fitting the two spectra to account for small collisional effects observed for the near zero probe delay spectrum. A dual-probe pure-rotational CARS system, in a 1D line-imaging configuration, is developed to demonstrate effectively the simultaneous temperature and pressure profiles recorded along the axial centerline of a highly underexpanded jet. The underexpanded air jet permits evaluation of this hybrid time-frequency domain approach for temperature and pressure measurements across a wide range of low-temperature-low-pressure conditions of interest in supersonic ground-test facilities. Single-laser-shot measurement precisions in both quantities and pressure measurement accuracy are systematically evaluated in the quiet zone upstream of the Mach disk. Precise thermometry approaching 1%-2% is observed in regions of high CARS signal-to-noise ratios. Pressure measurements are optimized at probe time delays where the ratio of the late probe delay to the Raman lifetime exceeds four (τ 2/τ R>4). The impact of low-temperature Raman linewidths on CARS pressure measurements is evaluated, and comparisons of CARS pressures obtained with our recent low-temperature pure-rotational Raman linewidth data and extrapolated high-temperature Q-branch linewidths are presented. Considering all measurements with τ 2/τ R≥4.0, measured pressures were on average 7.9% of the computed isentropic values with average shot-to-shot deviations representing a combination of instrument noise and fluid fluctuations of 5.0%.

Development of an image classification pipeline for atherosclerotic plaques assessment using supervised machine learning

BackgroundDuring atherosclerosis, the narrowing of the arterial lumen is observed through the accumulation of bio compounds and the formation of plaque within artery walls. A non-linear optical imaging modality (NLOM), coherent anti-stokes Raman scattering (CARS) microscopy, can be used to image lipid-rich structures commonly found in atherosclerotic plaques. By matching the lipid’s molecular vibrational frequencies (CH bonds), it is possible to map the accumulation of lipid-rich structures without the need for exogenous labelling and/or processing of the samples. CARS allows for the visualization of the morphological features of plaque. In combination with supervised machine learning, CARS imaged morphological features can be used to characterize the progression of atherosclerotic plaques. ResultsBased on a set of label-free CARS images of atherosclerotic plaques (i.e. foam cell clusters) from a Watanabe heritable hyperlipidemic rabbit model, we developed an automated pipeline to classify atherosclerotic lesions based on their major morphological features. Our method uses image preprocessing to first improve the quality of the CARS-imaged plaque, followed by the segmentation of the plaque using Otsu thresholding, marker-controlled watershed, K-means segmentation and a novel independent foam cell thresholding segmentation. To define relevant morphological features, 27 quantitative features were extracted and further refined by a novel coefficient of variation feature refinement method in accordance with filter-type feature selection. Refined morphological features were supplied into three supervised machine learning algorithms; K-nearest neighbour, support vector machine and decision tree classifier. The classification pipeline showcased the ability to exploit relevant plaque morphological features to accurately classify 3 pre-defined stages of atherosclerosis: early fatty streak development (EFS) and advancing atheroma (AA) with a greater than 85% class accuracyConclusionsThrough the combination of CARS microscopy and computational methods, a powerful classification tool was developed to identify the progression of atherosclerotic plaque in an automated manner. Using a curated dataset, the classification pipeline demonstrated the ability to differentiate between EFS, EF and AA. Thus, presenting the opportunity to classify the onset of atherosclerosis at an earlier stage of development

Zein-stabilized emulsions by ethanol addition; stability and microstructure

The aim of this research was to introduce a novel method to use zein protein and stabilize an ethanol-based emulsion. To study the effect of ethanol in the continuous phase of the emulsion on the stability and microstructure of the emulsions, ethanol content was tuned in the range of 50–90%v/v. Also, the effect of zein concentration on the stability of ethanol-based emulsions was examined. The microstructure and mean droplet size of emulsions were measured using static light scattering (SLS), with Coherent Anti-Stokes Raman Scattering (CARS) Microscopy combined with image processing. The results showed that the effect of ethanol concentration on the interfacial tension and zein solubility plays a substantial role in the formation, stability, and microstructure of emulsions. The data indicated that 70 %v/v ethanol content resulted in the lowest interfacial tension between oil and ethanol-water mixture in the presence of 10 %w/v zein. Therefore, the minimum mean droplet size was achieved at 70 %v/v ethanol equal to around 10 μm. Furthermore, increasing zein concentration decreased oil droplet mean size and enhanced the emulsion stability against Ostwald ripening. • Zein protein can stabilize oil in aqueous ethanol emulsion. • Ethanol quantity can affect the stability of zein-stabilized emulsion drastically. • The surface activity of zein was an interplay between zein solubility and effect of ethanol on interfacial tension.

Label-free multimodal imaging of infected Galleria mellonella larvae

Non-linear imaging modalities have enabled us to obtain unique morpho-chemical insights into the tissue architecture of various biological model organisms in a label-free manner. However, these imaging techniques have so far not been applied to analyze the Galleria mellonella infection model. This study utilizes for the first time the strength of multimodal imaging techniques to explore infection-related changes in the Galleria mellonella larvae due to massive E. faecalis bacterial infection. Multimodal imaging techniques such as fluorescent lifetime imaging (FLIM), coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second harmonic generation (SHG) were implemented in conjunction with histological HE images to analyze infection-associated tissue damage. The changes in the larvae in response to the infection, such as melanization, vacuolization, nodule formation, and hemocyte infiltration as a defense mechanism of insects against microbial pathogens, were visualized after Enterococcus faecalis was administered. Furthermore, multimodal imaging served for the analysis of implant-associated biofilm infections by visualizing biofilm adherence on medical stainless steel and ePTFE implants within the larvae. Our results suggest that infection-related changes as well as the integrity of the tissue of G. mellonella larvae can be studied with high morphological and chemical contrast in a label-free manner.

2-hydroxyisobutyric acid (2-HIBA) modulates ageing and fat deposition in Caenorhabditis elegans.

High levels of 2-hydroxyisobutyric acid (2-HIBA) were found in urines of patients with obesity and hepatic steatosis, suggesting a potential involvement of this metabolite in clinical conditions. The gut microbial origin of 2-HIBA was hypothesized, however its actual origin and role in biological processes are still not clear. We investigated how treatment with 2-HIBA affected the physiology of the model organism Caenorhabditis elegans, in both standard and high-glucose diet (HGD) growth conditions, by targeted transcriptomic and metabolomic analyses, Coherent Anti-Stokes Raman Scattering (CARS) and two-photon fluorescence microscopy. In standard conditions, 2-HIBA resulted particularly effective to extend the lifespan, delay ageing processes and stimulate the oxidative stress resistance in wild type nematodes through the activation of insulin/IGF-1 signaling (IIS) and p38 MAPK pathways and, consequently, through a reduction of ROS levels. Moreover, variations of lipid accumulation observed in treated worms correlated with transcriptional levels of fatty acid synthesis genes and with the involvement of peptide transporter PEP-2. In HGD conditions, the effect of 2-HIBA on C. elegans resulted in a reduction of the lipid droplets deposition, accordingly with an increase of acs-2 gene transcription, involved in β-oxidation processes. In addition, the pro-longevity effect appeared to be correlated to higher levels of tryptophan, which may play a role in restoring the decreased viability observed in the HGD untreated nematodes.

Nonlinear optical imaging by detection with optical parametric amplification (invited paper).

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions.

Fast-acting and injectable cryoneurolysis device

Cryoneurolysis is an opioid-sparing therapy for long-lasting and reversible reduction of pain. We developed a nerve-selective method for cryoneurolysis by local injection of ice-slurry (− 5 to − 6 °C) that induced decrease in nocifensive response starting from about a week after treatment and lasting up to 8 weeks. In this study, we test the hypothesis that injection of colder slurry leads to faster onset of analgesia. Colder slurry (− 9ºC) was injected around the rat sciatic nerve to induce cryoneurolysis. Hematoxylin and Eosin (H&E) staining was used to examine histologic effects on surrounding tissues. Coherent anti-Stokes Raman scattering (CARS) microscopy was used to study effects on myelin sheaths. Functional tests were used to assess changes in sensory and motor function in the treated hind paw. No inflammation or scarring was detected in surrounding skin and muscle tissues at day 7 post slurry injection. Functional tests showed rapid onset reduction in mechanical pain sensitivity starting from day 1 and lasting up to day 98. CARS imaging demonstrated disintegration of myelin sheaths post treatment followed by complete recovery of nerve structure by day 140. In this study we showed that colder slurry (− 9 °C) produces more rapid onset and longer duration of analgesia, while remaining nerve-selective

Long-Term Repeatable In Vivo Monitoring of Amyloid-β Plaques and Vessels in Alzheimer's Disease Mouse Model with Combined TPEF/CARS Microscopy.

Long-term, repeatable monitoring of the appearance and progress of Alzheimer's disease (AD) in real time can be extremely beneficial to acquire highly reliable diagnostic insights, which is crucial for devising apt strategies towards effective AD treatment. Herein, we present an optimized innovative cranial window imaging method for the long-term repeatable imaging of amyloid-β (Aβ) plaques and vessels in an AD mouse model. Basically, two-photon excitation fluorescence (TPEF) microscopy was used to monitor the fluorescently labeled Aβ plaques, whereas the label-free blood vessels were studied using coherent anti-Stokes Raman scattering (CARS) microscopy in the live in vivo AD mouse model. It was possible to clearly observe the Aβ deposition and vascular structure in the target cortex localization for 31 weeks in the AD mouse model using this method. The combined TPEF/CARS imaging studies were also instrumental in realizing the relationship between the tendency of Aβ deposition and ageing. Essentially, the progression of cerebral amyloid angiopathy (CAA) in the AD mouse model was quantitatively characterized, which revealed that the proportion Aβ deposition in the unit vessel can increase from 13.63% to 28.80% upon increasing the age of mice from 8 months old to 14 months old. The proposed imaging method provided an efficient, safe, repeatable platform with simple target localization aptitude towards monitoring the brain tissues, which is an integral part of studying any brain-related physiological or disease conditions to extract crucial structural and functional information.

Semi-synthetic data generation to fine-tune a convolutional neural network for retrieving Raman signals from CARS spectra

Coherent anti-Stokes Raman scattering (CARS) is a well-known third-order non-linear spectroscopic technique utilized to analyze the molecule structures. However, it has a coherent non-resonant background (NRB) alongside with Raman resonant, thus, it is difficult to extract the Raman part. In this work, the Raman signal is extracted from the CARS spectrum by using a convolutional neural network. The model architecture is adapted from the original SpecNet model. The model is pre-trained with synthetic data and fine-tuned with semi-synthetic data based on two sets of semi-synthetic spectra. The experimental results show that the model achieves 86% accuracy in predicting the Raman signal of semi-synthetic data. In addition, the sensitivity of the model performance to varying levels of noise is analysed. Based on standard metrics, the model performance decreases with the increasing level of noise in a non-linear manner. Finally, the prediction capability of the fine-tuned SpecNet model was evaluated on the four experimental CARS spectra and the results were found to be better compared to SpecNet.