High-sensitivity vibrational imaging with frequency modulation coherent anti-Stokes Raman scattering (FM CARS) microscopy
ABSTRACT We demonstrate a new approach to coherent anti-Stokes Raman scattering (CARS) microscopy that significantly increases the detection sensitivity. CARS signals are generated by collinearly overlapped, tightly focused, and raster scanned pump and Stokes laser beams, whose difference frequency is rapidly modulated. The resulting amplitude modulation of the CARS signal is detected through a lock-in amplifier. This scheme efficiently suppresses the nonresonant background and allows for the detection of far fewer vibrational oscillators than possible through existing CARS microscopy methods.
Full-textDOI: · Available from: Conor Evans, Mar 21, 2014
- SourceAvailable from: Charles Henry Camp Jr
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- "This coherent mixing, however, does bring spectral distortions. For CARS microscopies that probe small increments of the full vibrational spectrum, physical methods are utilized to reduce the NRB generation, which in turn reduces the overall CARS signal    . For spectroscopic CARS techniques (microspectroscopies), however, two classes of numerical methods are commonly used to remove the distortion of the NRB: one based on maximizing entropy  and the other utilizing the Kramers-Kronig (KK) relation . "
ABSTRACT: Coherent anti-Stokes Raman scattering (CARS) microspectroscopy has demonstrated significant potential for biological and materials imaging. To date, however, the primary mechanism of disseminating CARS spectroscopic information is through pseudocolor imagery, which explicitly neglects a vast majority of the hyperspectral data. Furthermore, current paradigms in CARS spectral processing do not lend themselves to quantitative sample-to-sample comparability. The primary limitation stems from the need to accurately measure the so-called nonresonant background (NRB) that is used to extract the chemically-sensitive Raman information from the raw spectra. Measurement of the NRB on a pixel-by-pixel basis is a nontrivial task; thus, reference NRB from glass or water are typically utilized, resulting in error between the actual and estimated amplitude and phase. In this manuscript, we present a new methodology for extracting the Raman spectral features that significantly suppresses these errors through phase detrending and scaling. Classic methods of error-correction, such as baseline detrending, are demonstrated to be inaccurate and to simply mask the underlying errors. The theoretical justification is presented by redeveloping the theory of phase retrieval via the Kramers-Kronig relation, and we demonstrate that these results are also applicable to maximum entropy method-based phase retrieval. This new error-correction approach is experimentally applied to glycerol spectra and tissue images, demonstrating marked consistency between spectra obtained using different NRB estimates, and between spectra obtained on different instruments. Additionally, in order to facilitate implementation of these approaches, we have made many of the tools described herein available free for download.
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- "The other corner is occupied by narrowband coherent anti-Stokes Raman scattering (CARS) with just one spectral discriminating line, quadratic concentration dependence and a non-resonant background, but with tremendous speed. In between we fi nd a variety of techniques such as spectrally scanning CARS (Ganikhanov et al. 2006, Kano 2010 ), amplitude shaped stimulated Raman scattering (SRS) (Xie et al. 2011 ) and different incarnations of broadband CARS (Rinia et al. 2007, Motzkus et al. 2009, Parekh et al. 2010 ). In the Optical Sciences Group at the University of Twente we have pursued the use of phase to improve selectivity without sacrifi cing speed in both narrowband and broadband CARS, both of which will be considered in this review. "
ABSTRACT: The phase of the molecular response can be exploited to improve selectivity without sacrifi cing speed in both narrow-band and broadband coherent anti-Stokes Raman scattering (CARS) microscopy, both of which will be considered in this review of the work that was performed in our group.03/2012; 31(1). DOI:10.1515/revac.2011.122
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- "This non-resonant background exists even in pure substances because multiple pathways exist to the CARS wavelength. Many approaches have been developed to reduce this background, such as polarisation control (Oudar et al., 1979; Yuratich and Hanna, 1977), time-delay (Volkmer et al., 2002; von Vacano and Motzkus, 2006), frequency modulation (Ganikhanov et al., 2006) and interferometric detection (Cheng, 2007; Evans et al., 2004; Müller and Zumbusch, 2007). The last has the advantage that the amplitude and phase are detected, where the amplitude is linear in the number (density) of the molecules. "
ABSTRACT: Non-linear optics encompasses a range of optical phenomena, including two- and three-photon fluorescence, second harmonic generation (SHG), sum frequency generation (SFG), difference frequency generation (DFG), third harmonic generation (THG), coherent anti-Stokes Raman scattering (CARS), and stimulated Raman scattering (SRS). The combined advantages of using these phenomena for imaging complex pharmaceutical systems include chemical and structural specificities, high optical spatial and temporal resolutions, no requirement for labels, and the ability to image in an aqueous environment. These features make such imaging well suited for a wide range of pharmaceutical and biopharmaceutical investigations, including material and dosage form characterisation, dosage form digestion and drug release, and drug and nanoparticle distribution in tissues and within live cells. In this review, non-linear optical phenomena used in imaging will be introduced, together with their advantages and disadvantages in the pharmaceutical context. Research on pharmaceutical and biopharmaceutical applications is discussed, and potential future applications of the technology are considered.International Journal of Pharmaceutics 12/2010; 417(1-2):163-72. DOI:10.1016/j.ijpharm.2010.12.017 · 3.65 Impact Factor