Compressive Raman imaging with spatial frequency modulated illumination

Superpixels meet essential spectra for fast Raman hyperspectral microimaging

In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

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.

Design and analysis of polygonal mirror-based scan engines for improved spatial frequency modulation imaging

Spatial frequency modulation imaging (SPIFI) is a structured illumination single pixel imaging technique that is most often achieved via a rotating modulation disk. This implementation produces line images with exposure times on the order of tens of milliseconds. Here, we present a new architecture for SPIFI using a polygonal scan mirror with the following advances: (1) reducing SPIFI line image exposure times by 2 orders of magnitude, (2) facet-to-facet measurement and correction for polygonal scan design, and (3) a new anamorphic magnification scheme that improves resolution for long working distance optics.

Compressed Raman method combining classification and estimation of spectra with optimized binary filters

Compressed Raman methods allow classification between known chemical species with only a few measurements through binary filters. We propose a methodology for binary filter optimization, in which filters are modified at each pixel so that classification can still be achieved pixel by pixel with a few measurements acquired in parallel, while retaining the ability to reconstruct a full spectrum when combining measurements from several pixels. This approach is robust to intensity variations between pixels. It relies on a generalized Bhattacharyya bound and on the Cramér-Rao bound to tailor filters with optimized performance.

Compressed sensing in the far-field of the spatial light modulator in high noise conditions

Single-pixel imaging techniques as an alternative to focal-plane detector arrays are being widely investigated. The interest in these single-pixel techniques is partly their compatibility with compressed sensing but also their applicability to spectral regions where focal planes arrays are simply not obtainable. Here, we show how a phased-array modulator source can be used to create Hadamard intensity patterns in the far-field, thereby enabling single-pixel imaging. Further, we successfully illustrate an implementation of compressed sensing for image reconstruction in conditions of high noise. In combination, this robust technique could be applied to any spectral region where spatial light phase modulators or phased-array sources are available.

Line-scan compressive Raman imaging with spatiospectral encoding

We report a line-scanning imaging modality of compressive Raman technology with a single-pixel detector. The spatial information along the illumination line is encoded onto one axis of a digital micromirror device, while spectral coding masks are applied along the orthogonal direction. We demonstrate imaging and classification of three different chemical species.

Spatial frequency modulated imaging in coherent anti-Stokes Raman microscopy

For sparse samples or in the presence of ambient light, the signal-to-noise ratio (SNR) performance of single-point-scanning coherent anti-Stokes Raman scattering (CARS) images is not optimized. As an improvement, we propose replacing the conventional CARS focus-point illumination with a periodically structured focus line while continuing to collect the transmitted CARS intensity on a single detector. The object information along the illuminated line is obtained by numerically processing the CARS signal recorded for various periods of the structured focus line. We demonstrate experimentally the feasibility of this spatial frequency modulated imaging (SPIFI) in CARS (SPIFI-CARS) and SHG (SPIFI-SHG) and identify situations where its SNR is better than that of the single-point-scanning approach.

Imaging and quantifying drug delivery in skin - Part 2: Fluorescence andvibrational spectroscopic imaging methods

Understanding the delivery and diffusion of topically-applied drugs on human skin is of paramount importance in both pharmaceutical and cosmetics research. This information is critical in early stages of drug development and allows the identification of the most promising ingredients delivered at optimal concentrations to their target skin compartments. Different skin imaging methods, invasive and non-invasive, are available to characterize and quantify the spatiotemporal distribution of a drug within ex vivo and in vivo human skin. The first part of this review detailed invasive imaging methods (autoradiography, MALDI and SIMS). This second part reviews non-invasive imaging methods that can be applied in vivo: i) fluorescence (conventional, confocal, and multiphoton) and second harmonic generation microscopies and ii) vibrational spectroscopic imaging methods (infrared, confocal Raman, and coherent Raman scattering microscopies). Finally, a flow chart for the selection of imaging methods is presented to guide human skin ex vivo and in vivo drug delivery studies.

Compressed Raman classification method with upper-bounded error probability

Classification of different species with Raman measurements is analyzed when a total of exactly $ N $N photons are detected with binary filtered Raman spectra instead of fixing the measuring time. The optimal classification method for this problem leads to classification error probabilities upper-bounded by the Bhattacharyya bound and that are invariant to the multiplication of the spectrum intensities by an unknown factor. Furthermore, it is shown that this approach can be implemented with a number of binary filters smaller than the number of species to discriminate.