A clear correlation has been observed between the resonance Raman (RR) spectra of plaques in the aortic tunica intimal wall of a human corpse and three states of plaque evolution: fibrolipid plaques, calcified and ossified plaques, and vulnerable atherosclerotic plaques (VPs). These three states of atherosclerotic plaque lesions demonstrated unique RR molecular fingerprints from key molecules, rendering their spectra unique with respect to one another. The vibrational modes of lipids, cholesterol, carotenoids, tryptophan and heme proteins, the amide I, II, III bands, and methyl/methylene groups from the intrinsic atherosclerotic VPs in tissues were studied. The salient outcome of the investigation was demonstrating the correlation between RR measurements of VPs and the thickness measurements of fibrous caps on VPs using standard histopathology methods, an important metric in evaluating the stability of a VP. The RR results show that VPs undergo a structural change when their caps thin to 66 μm, very close to the 65-μm empirical medical definition of a thin cap fibroatheroma plaque, the most unstable type of VP.
Compressive sensing technology can theoretically be used to develop low cost compact spectrometers with the
performance of larger and more expensive systems. Indeed, compressive sensing for spectroscopic systems has been
previously demonstrated using coded aperture techniques, wherein a mask is placed between the grating and a charge
coupled device (CCD) and multiple measurements are collected with different masks. Although proven effective for
some spectroscopic sensing paradigms (e.g. Raman), this approach requires that the signal being measured is static
between shots (low noise and minimal signal fluctuation). Many spectroscopic techniques applicable to remote sensing
are inherently noisy and thus coded aperture compressed sensing will likely not be effective. This work explores an
alternative approach to compressed sensing that allows for reconstruction of a high resolution spectrum in sensing
paradigms featuring significant signal fluctuations between measurements. This is accomplished through relatively
minor changes to the spectrometer hardware together with custom super-resolution algorithms. Current results indicate
that a potential overall reduction in CCD size of up to a factor of 4 can be attained without a loss of resolution. This
reduction can result in significant improvements in cost, size, and weight of spectrometers incorporating the technology.
Resonance Raman (RR) spectroscopic technique has a high potential for label-free and in-situ detection of biomedical lesions in vivo. This study evaluates the ability of RR spectroscopy method as an optical histopathology tool to detect the atherosclerotic plaque states of abdominal aorta in vitro. This part demonstrates the RR spectral molecular fingerprint features from different sites of the atherosclerotic abdominal aortic wall tissues. Total 57 sites of five pieces aortic samples in intimal and adventitial wall from an autopsy specimen were examined using confocal micro Raman system of WITec 300R with excitation wavelength of 532nm. The preliminary RR spectral biomarkers of molecular fingerprints indicated that typical calcified atherosclerotic plaque (RR peak at 964cm-1) tissue; fibrolipid plaque (RR peaks at 1007, 1161, 1517 and 2888cm-1) tissue, lipid pool with the fatty precipitation cholesterol) with collagen type I (RR peaks at 864, 1452, 1658, 2888 and 2948cm-1) in the soft tissue were observed and investigated.
Hyperspectral imaging (HSI) systems have the potential for tremendous military utility. The ability to use detailed spectral information to characterize objects that may be sub-pixel in size enables efficient surveillance and terrain characterization over wide areas. TRW is actively involved in all aspects of HSI systems form building hyperspectral sensors and real-time processors to the development of processing algorithms. This paper presents performance results on hyperspectral imagery data for target detection. Whereas a recent paper presented results for the detection of known targets, this paper focuses on anomaly detection algorithms for application in those situations involving a high degree of target uncertainty or poor knowledge of atmospheric effects.
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