Quantum optical approaches to biological measurement could enable unprecedented sensitivity, specificity, and resolution with minimal sample perturbation. Additionally, quantum optics provides new “knobs to turn” in imaging and spectroscopy that are not approachable with classical means. One such example is through entangled photons, in which groups of photons have properties that are intrinsically and inseparably linked. In this presentation, I will provide an introduction to entangled light applications, our experimental work in two-photon absorption with entangled light, and our experimental work establishing that even post-traversal through micrometers and millimeters of biological tissue/media, time-energy entangled light can maintain this unique linkage.
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.
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.
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.
We demonstrate that pulse shaping of a narrowband pulse can suppress the nonresonant background (NRB) contribution and retrieve resonant Raman signals efficiently in a broadband coherent anti-Stokes Raman scattering (CARS) spectrum. A pulse shaper prepares a probe pulse with two spectral components of differing phase. When the CARS fields
generated by these two out-of-phase components are optically mixed, the NRB signal is greatly reduced while a resonant CARS signal survives with minimal attenuation. We discuss three model schemes for the interfering pulse components: (1) two pulses with different bandwidths and the same center frequency (ps-fs scheme); (2) two pulses with the same bandwidth and shifted center frequencies
(ps-ps scheme); and (3) a pulse with different phases across the center frequency (fs(+/-) scheme). In all schemes, only the resonant signal from the "3-color" CARS mechanism survives. The resonant signal from "2-color" CARS mechanism vanishes along with the NRB. We discuss optimization conditions for signal intensity and shape of resonant CARS peaks.
Broadband coherent anti-Stokes Raman scattering (CARS) microscopy promises non-invasive, high information content microscopic imaging for live cells and tissues. Generation of a broadband continuum with
appropriate characteristics to be used for Stokes light has been a roadblock for bringing this promise to fruition. Here we present numerical and experimental work towards generation of a suitable Stokes light continuum from a femtosecond pulse laser. In the simulations, the pulse propagation along the fiber is governed by the generalized nonlinear Schroedinger equation, including linear effects from the group velocity dispersion and the nonlinear effects from self phase modulation, delayed Raman scattering process and
self-steepening. The equations are integrated using a symmetrized split-step Fourier method. Optimal fiber-related simulation parameters used in the model, such as the nonlinear coefficient, dispersion coefficients and the fraction of the stimulated Raman scattering contribution etc, are systematically investigated and determined for the GeO2 doped fiber.
Self-location is the capability of a mobile robot to determine its position in the environment referring to absolute landmarks. The possibility to use natural visual landmarks for self-location augments the autonomy and the flexibility of mobile vehicles. In this paper the use of junctions, detected in real images, as landmarks is proposed. The use of visual cues means that problems regarding variations of perspective and scale must be resolved. We propose to formulate the junction recognition as a graph matching problem and resolved using standard methods. Experimental results are shown on real contexts.
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