We are currently investigating a non-invasive technique to determine collagen content and hydration in the skin by the optical methods of absorption, fluorescence and Raman spectroscopy for imaging. A major barrier to the effective use of skin therapies is the difficulty of quantifying existing collagen content and water content. Absorption of near infrared light by skin depends both on the concentration of collagen and the amount of water in the skin. In the near infrared (NIR window I) collagen and water have similar absorption profiles. However, because the infrared spectrum of collagen and water from 900 nm to 1700 nm (window II and III) are significantly different, it allows us to quantify collagen relative to water content. The ratio of the absorption of collagen normalized to water at 1700 nm and at 1950 nm (window III and window IV) is linear in collagen concentration. This can be used to discriminate between tissues by absorption imaging. We compare these results to Raman spectroscopy and native fluorescence. Our goal is to generate data that can be used for qualitative imaging allowing for improvement in assessing the effectiveness of skin-treatment therapies for the health care field to develop a device for home and medical office which can answer the age-old question: “Mirror, Mirror on the wall, where exactly should I apply skin therapeutics for maximum effectiveness and minimal side-effects?”
In gastrointestinal endoscopic surgery, bleeding from the accidental resection of hidden vessels is a major complication, requiring immediate conversion to open surgery. Methods of visualizing occult vessels have been proposed using the FDA approved fluorescent dye Indocyanine green (ICG), but Native label-free fluorescence of the submucosa—present up till about 886 nm—prevents the use ICG in near-infrared (NIR) window I (700 nm to 900 nm). Instead, absorption imaging is preferred; the darker vessels are visible to 4.5 mm deep. Using data from Raman scattering, absorption, native fluorescence, SHG and the photon excitation fluorescence we investigate the spectral properties and propose optimal parameters for differentiation of blood vessels from surrounding tissue in a variety of tissue types in NIR window II (1000 nm to 1350 nm) and NIR window III (1550 nm to 1900 nm, the “Golden Window”) as a complement to absorption imaging.
Injuries to main vascular structures within the sub mucosa present a serious complication during surgery. There is no evidence-based treatment to prevent this type of injury, so detection is critical. Using a combination of absorption and fluorescence imaging we can detect blood vessel phantoms to a depth of 7 mm in intestinal sub-mucosa. Using an illumination source at 850, and reading the cross-polarized reflected signal also at 850 gives the absorption image. Simultaneous excitation of ICG at 785 nm creates a fluorescent response that is used for contrast enhancement.
Laser speckle from particles that are smaller than the wavelength of light resemble a random Gaussian field, but can be shown to contain a characteristic spectrum in frequency space. Speckle is caused by not only the instantaneous microstructure of nanoparticles in suspension that will fluctuate as they reorganize, but also by the magnetic and optical properties of the scattering medium itself. Here we demonstrate interactive tool that can be used to define similarities between seemingly random scattering fields. Optimization of the Fourier spatial frequency spectrum gives a representative pattern that can be directly correlated to the transport properties of the particles.
Drug delivery to tumors is well known to be chaotic and limited, partly from dysfunctional vasculature, but also because of microscopic regional variations in composition. Modeling the of transport of nanoparticle therapeutics, therefore must include not only a description of vascular permeability, but also of the movement of the drug as suspended in tumor interstitial fluid (TIF) once it leaves the blood vessel. Understanding of this area is limited because we currently lack the tools and analytical methods to characterize it. We have previously shown that directional anisotropy of drug delivery can be detected using Directional Fourier Spatial Frequency (DFSF) Analysis. Here we extend this approach to generate flow line maps of nanoparticle transport in TIF relative to tumor ultrastructure, and show that features of tumor spatial heterogeneity can be identified that are directly related to local flow isometries. The identification of these regions of limited flow may be used as a metric for determining response to therapy, or for the optimization of adjuvant therapies such as radiation pre-treatment, or enzymatic degradation.
The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.
Atherosclerosis is characterized by the growth of fibrous plaques due to the retention of cholesterol and lipids within the artery wall, which can lead to vessel occlusion and cardiac events. One way to evaluate arterial disease is to quantify the amount of lipid present in these plaques, since a higher disease burden is characterized by a higher concentration of lipid. Although therapeutic stimulation of reverse cholesterol transport to reduce cholesterol deposits in plaque has not produced significant results, this may be due to current image analysis methods which use averaging techniques to calculate the total amount of lipid in the plaque without regard to spatial distribution, thereby discarding information that may have significance in marking response to therapy. Here we use Directional Fourier Spatial Frequency (DFSF) analysis to generate a characteristic spatial frequency spectrum for atherosclerotic plaques from C57 Black 6 mice both treated and untreated with a cholesterol scavenging nanoparticle. We then use the Cauchy product of these spectra to classify the images with a support vector machine (SVM). Our results indicate that treated plaque can be distinguished from untreated plaque using this method, where no difference is seen using the spatial averaging method. This work has the potential to increase the effectiveness of current in-vivo methods of plaque detection that also use averaging methods, such as laser speckle imaging and Raman spectroscopy.
Directional Fourier spatial frequency analysis was used on standard histological sections to identify salient directional bias in the spatial frequencies of stromal and epithelial patterns within tumor tissue. This directional bias is shown to be correlated to the pathway of reduced fluorescent tracer transport. Optical images of tumor specimens contain a complex distribution of randomly oriented aperiodic features used for neoplastic grading that varies with tumor type, size, and morphology. The internal organization of these patterns in frequency space is shown to provide a precise fingerprint of the extracellular matrix complexity, which is well known to be related to the movement of drugs and nanoparticles into the parenchyma, thereby identifying the characteristic spatial frequencies of regions that inhibit drug transport. The innovative computational methodology and tissue validation techniques presented here provide a tool for future investigation of drug and particle transport in tumor tissues, and could potentially be used a priori to identify barriers to transport, and to analyze real-time monitoring of transport with respect to therapeutic intervention.
The optical spatial frequencies of tumor interstitial fluid (TIF) are investigated. As a concentrated colloidal suspension of interacting native nanoparticles, the TIF can develop internal ordering under shear stress that may hinder delivery of antitumor agents within tumors. A systematic method is presented to characterize the TIF nanometer-scale microstructure in a model suspension of superparamagnetic iron-oxide nanoparticles and reconstituted high-density lipoprotein by Fourier spatial frequency (FSF) analysis so as to differentiate between jammed and fluid structural features in static transmission electron microscope images. The FSF method addresses one obstacle faced in achieving quantitative dosimetry to neoplastic tissue, that of detecting these nanoscale barriers to transport, such as would occur in the extravascular space immediately surrounding target cells.
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