Charles H. Camp

High-speed coherent Raman fingerprint imaging of biological tissues

An imaging platform based on broadband coherent anti-Stokes Raman scattering (BCARS) has been developed which provides an advantageous combination of speed, sensitivity and spectral breadth. The system utilizes a configuration of laser sources that probes the entire biologically-relevant Raman window (500 cm−1 to 3500 cm−1) with high resolution (< 10 cm−1). It strongly and efficiently stimulates Raman transitions within the typically weak “fingerprint” region using intrapulse 3-colour excitation, and utilizes the nonresonant background (NRB) to heterodyne amplify weak Raman signals. We demonstrate high-speed chemical imaging in two- and three-dimensional views of healthy murine liver and pancreas tissues and interfaces between xenograft brain tumours and the surrounding healthy brain matter.

Multicomponent Chemical Imaging of Pharmaceutical Solid Dosage Forms with Broadband CARS Microscopy

We compare a coherent Raman imaging modality, broadband coherent anti-Stokes Raman scattering (BCARS) microscopy, with spontaneous Raman microscopy for quantitative and qualitative assessment of multicomponent pharmaceuticals. Indomethacin was used as a model active pharmaceutical ingredient (API) and was analyzed in a tabulated solid dosage form, embedded within commonly used excipients. In comparison with wide-field spontaneous Raman chemical imaging, BCARS acquired images 10× faster, at higher spatiochemical resolution and with spectra of much higher SNR, eliminating the need for multivariate methods to identify chemical components. The significant increase in spatiochemical resolution allowed identification of an unanticipated API phase that was missed by the spontaneous wide-field method and bulk Raman spectroscopy. We confirmed the presence of the unanticipated API phase using confocal spontaneous Raman, which provided spatiochemical resolution similar to BCARS but at 100× slower acquisition times.

Quantitative, comparable coherent anti‐Stokes Raman scattering (CARS) spectroscopy: correcting errors in phase retrieval

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 re-developing 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.

Beam scanning for rapid coherent Raman hyperspectral imaging.

Coherent Raman imaging requires high-peak power laser pulses to maximize the nonlinear multiphoton signal generation, but accompanying photo-induced sample damage often poses a challenge to microscopic imaging studies. We demonstrate that beam scanning by a 3.5-kHz resonant mirror in a broadband coherent anti-Stokes Raman scattering (BCARS) imaging system can reduce photo-induced damage without compromising signal intensity. Additionally, beam scanning enables slit acquisition, in which spectra from a thin line of sample illumination are acquired in parallel during a single charge-coupled device exposure. Reflective mirrors are employed in the beam-scanning assembly to minimize chromatic aberration and temporal dispersion. The combined approach of beam scanning and slit acquisition is compared with the sample-scanning mode in terms of spatial resolution, photo-induced damage, and imaging speed at the maximum laser power below the sample-damage threshold. We show that the beam-scanning BCARS imaging method can reduce photodamage probability in biological cells and tissues, enabling faster imaging speed by using a higher excitation laser power than could be achieved without beam scanning.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks.

HighlightsThe problem of accurate and interpretable labeling of spectral images is addressed.A tandem of Artificial Neural Network (ANN) models is designed to achieve accuracy.Labelling rules are generated from the tandem to deliver an interpretable modelThe labeling method was evaluated on labeled pixels and reference rules.The labeling accuracy was determined to be 85% (pixels) and 96% (rules). Graphical abstract Figure. No Caption available. Abstract This paper addresses the problem of classifying materials from microspectroscopy at a pixel level. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels. We approach the problem by designing a supervised classifier from a tandem of Artificial Neural Network (ANN) models that identify relevant features in raw spectra and achieve high classification accuracy. The tandem of ANN models is meshed with classification rule extraction methods to lower the model complexity and to achieve interpretability of the resulting model. The contribution of the work is in designing each ANN model based on the microspectroscopy hypothesis about a discriminatory feature of a certain target class being composed of a linear combination of spectra. The novelty lies in meshing ANN and decision rule models into a tandem configuration to achieve accurate and interpretable classification results. The proposed method was evaluated using a set of broadband coherent anti‐Stokes Raman scattering (BCARS) microscopy cell images (600 000 pixel‐level spectra) and a reference four‐class rule‐based model previously created by biochemical experts. The generated classification rule‐based model was on average 85% accurate measured by the DICE pixel label similarity metric, and on average 96% similar to the reference rules measured by the vector cosine metric.

From spectroscopy to chemical imaging: machine learning for hyperspectral coherent Raman imagery (Conference Presentation)

Coherent Raman hyperspectral imaging technologies have progressed dramatically in recent years, collecting 100’s to 10,000’s of spectra per second with the spectra breadth of traditional spontaneous Raman spectroscopy. There is, however, a lack in available analysis and processing capabilities to bridge the gap between spectroscopy and chemical imaging, in which the end-user is interacting with molecular targets of interest. In this talk we will discuss our latest developments towards this goal, in particular: spectral unmixing/endmember extraction methods and high-speed, high-throughput peak characterization (peak-finding and fitting). Spectral unmixing methods aim to uncover pure species spectra. Certain demonstrated methods, such as vertex component analysis (VCA) require at least 1 pure pixel per chemical is present in the image. Other methods rely on statistical or geometric methods to estimate the pure spectra when no pure pixels are present. In this presentation, we will quantitatively compare results using several state-of-the-art techniques (internally and externally-developed). To autonomously examine retrieved pure spectra, we have developed a high-speed peak finding and fitting algorithm capable of characterizing spectra in micro- to milliseconds, in order to interface with our developed database and data mining methods. Collectively, these developments enable high-speed, high throughput analysis of 1 or many images. Numerical and experimental demonstrations will be presented on an open-source numerical tissue phantom and ~900-color BCARS imagery of murine tissue and clinical specimens.

Broadband coherent Raman imaging as a potential diagnostic aid for histopathology (Conference Presentation)

We will report on application of broadband coherent anti-Stokes Raman scattering microscopy1 to chemical mapping and characterization of resected prostate sections. While incidence of prostate cancer is very high, only a small fraction of prostate tumors will progress to advanced, metastatic disease and become dangerous, but prostatectomy and follow-on treatment have many undesirable potential side effects. Thus, it is important to predict which tumors will progress and which should be removed, but there is currently no highly reliable way to make such predictions. We will present a retrospective coherent Raman imaging study resected prostate sections focusing on locating tissue regions that present the highest diagnostic value with respect to lethal vs indolent disease. We intend that this provide a guide to optimal spectral sampling of these tissues to address this important clinical problem.

Microscopy: A larger palette for biological imaging

&NA; Biological molecules are often imaged by attaching fluorescent labels — but only a few label types can be used at a time. A method that could smash the record for the number of labels that can be used together is now reported. See Letter p.465

Rapid label-free chemical imaging of cells and tissues(Conference Presentation)

Coherent Raman imaging methods have been under development for almost 15 years. The field is beginning to mature, transitioning from a “new techniques” phase to an applications phase. I will discuss current capabilities of broadband coherent anti-Stokes Raman scattering (BCARS) microscopy using optimized excitation paradigms, and provide a few examples of how broadband BCARS imaging has helped to answer (or raise) questions in investigations of tissues and small organisms. I will also discuss progress in processing BCARS spectra to make them independent of excitation profile or non-resonant response, and directly comparable to spontaneous Raman spectra. I will also discuss progress on a new approach to time-domain BCARS that promises to significantly simplify and speed BCARS data acquisition.