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The modulations appearing on the backscattering spectrum originating from a scatterer are related to its diameter as described by Mie theory for spherical particles. Many metrics for Spectroscopic Optical Coherence Tomography (SOCT) take advantage of this observation in order to enhance the contrast of Optical Coherence Tomography (OCT) images. However, none of these metrics has achieved high accuracy when calculating the scatterer size. In this work, Mie theory was used to further investigate the relationship between the degree of modulation in the spectrum and the scatterer size. From this study, a new spectroscopic metric, the bandwidth of the Correlation of the Derivative (COD) was developed which is more robust and accurate, compared to previously reported techniques, in the estimation of scatterer size. The self-normalizing nature of the derivative and the robustness of the first minimum of the correlation as a measure of its width, offer significant advantages over other spectral analysis approaches especially for scatterer sizes above 3 μm. The feasibility of this technique was demonstrated using phantom samples containing 6, 10 and 16 μm diameter microspheres as well as images of normal and cancerous human colon. The results are very promising, suggesting that the proposed metric could be implemented in OCT spectral analysis for measuring nuclear size distribution in biological tissues. A technique providing such information would be of great clinical significance since it would allow the detection of nuclear enlargement at the earliest stages of precancerous development.

A seven-zone pupil filter which can extend the depth of focus (DOF) and provide lateral super resolution in Optical Coherence Tomography is designed. Both amplitude and phase of the transmitted light beam are modified in each zone. The scalar diffraction theory is used to optimize the zone parameters. A broadband source spectrum and a Gaussian beam profile has been taken into account for the design of this filter. Several filters have been reported to extend the depth of focus (DOF) and improve the resolution but they take the assumption that a monochromatic and uniform light beam is used. With the proposed filter the DOF in OCT can be extended by 14 times while the lateral resolution can be improved by a factor of 1.47 and maintained constant within this DOF.

Optical Coherence Tomography (OCT) images exhibit the effects of speckle which can make image interpretation and quantitative measurements difficult. Many approaches have been developed to reduce this speckle including both hardware implementations and post-processing techniques. However, they either suffer from a loss in resolution and blurring of the image or an increase in complexity and reduction in speed of the system. Wavelet decomposition has been shown to effectively separate the resolvable features of an image from the speckle pattern. The two components can then be processed separately. The speckle pattern can be filtered and then recombined with the resolvable component to create an image with improved signal-to-noise-ratio (SNR) and intact image details. The results of this algorithm are demonstrated on in vivo OCT images of skin taken with a swept-source based system. Such a technique, when applied, for example, to OCT images of disease, can be extremely useful in improving the clinical interpretation of the images as well as allowing more accurate quantitative measurements not affected by the presence of speckle.

Due to its effectiveness and broad coverage, Ciprofloxacin is the fifth most prescribed antibiotic in the US. As current methods of infection diagnosis and antibiotic sensitivity testing (i.e. an antibiogram) are very time consuming, physicians prescribe ciprofloxacin before obtaining antibiogram results. In order to avoid increasing resistance to the antibiotic, a method was developed to provide both a rapid diagnosis and the sensitivity to the antibiotic. Using Surface Enhanced Raman Spectroscopy, an antibiogram was obtained after exposing the bacteria to Ciprofloxacin for just two hours. Spectral analysis revealed clear separation between sensitive and resistant bacteria and could also offer some inside into the mechanisms of resistance.

The inappropriate use of antibiotics leads to antibiotic resistance, which is a major health care problem. The current method for determination of bacterial susceptibility to antibiotics requires overnight cultures. However most of the infections cannot wait for the results to receive treatment, so physicians administer general spectrum antibiotics. This results in ineffective treatments and aggravates the rising problem of antibiotic resistance. In this work, a rapid method for diagnosis and antibiogram for a bacterial infection was developed using Surface Enhanced Raman Spectroscopy (SERS) with silver nanoparticles. The advantages of this novel method include its rapidness and efficiency which will potentially allow doctors to prescribe the most appropriate antibiotic for an infection. SERS spectra of three species of gram negative bacteria, Escherichia coli, Proteus spp., and Klebsiella spp. were obtained after 0 and 4 hour exposure to the seven different antibiotics. Bacterial strains were diluted in order to reach the concentration of (2x10 5 cfu/ml), cells/ml which is equivalent to the minimum concentration found in urine samples from UTIs. Even though the concentration of bacteria was low, species classification was achieved with 94% accuracy using spectra obtained at 0 hours. Sensitivity or resistance to antibiotics was predicted with 81%-100% accuracy from spectra obtained after 4 hours of exposure to the different antibiotics. This technique can be applied directly to urine samples, and with the enhancement provided by SERS, this method has the potential to be developed into a rapid method for same day UTI diagnosis and antibiogram.

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. In this paper, we propose the use of Rank Order Kernels to classify bacterial samples. Rank Order Kernels are two-dimensional image functions which operate on two-dimensional images. The first step in the classification therefore, is to transform the Raman spectra to two-dimensional images. This is achieved by splitting the spectra into segments and calculating the ratio between the mean value of each and every other segment. This creates a two-dimensional matrix of ratios for each Raman spectrum. A similarity metric based on rank order kernels operating on the two-dimensional matrices is then used with a nearest neighbor algorithm for classification. Our results show that this method is comparable in accuracy to other methods which were used previously for the same data set.

There are three stages to a complete UTI diagnosis: (1) identification of a urine sample as positive/negative for an infection, (2) identification of the responsible bacterium, (3) antibiogram to determine the antibiotic to which the bacteria are most sensitive to. Using conventional methods, all three stages require bacterial cultures in order to provide results. This long delay in diagnosis causes a rise in ineffective treatments, chronic infections, health care costs and antibiotic resistance. In this work, SERS is used to identify a sample as positive/negative for a UTI as well as to obtain an antibiogram against different antibiotics. SERS spectra of serial dilutions of E. coli bacteria mixed with silver nanoparticles, showed a linear correlation between spectral intensity and concentration. For antibiotic sensitivity testing, SERS spectra of three species of gram negative bacteria were collected four hours after exposure to the antibiotics ciprofloxacin and amoxicillin. Spectral analysis revealed clear separation between bacterial samples exposed to antibiotics to which they were sensitive and samples exposed to antibiotics to which they were resistant. With the enhancement provided by SERS, the technique can be applied directly to urine samples leading to the development of a new, rapid method for UTI diagnosis and antibiogram.

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. The first stage in the classification of Raman spectra is commonly some form of preprocessing. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose the use of Support Vector Machines with a novel correlation kernel. Results, obtained from the analysis of Raman spectra of bacteria, illustrate that the correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing, even on highly-noisy data obtained using inexpensive equipment. In addition, the performance does not degrade when applied to distinct test sets, a key feature of a clinically viable diagnostic application of Raman Spectroscopy.

Various gold nanostructures are being investigated for medical and biological uses. For many medical applications, it would be beneficial to use near infrared (NIR) excitation as well as small gold nanospheres which can easily reach the cytoplasm and cell nucleus. For that purpose, we propose a novel nanostructure, the "shell aggregate," which consists of small nanospheres aggregated around a core such as an intracellular organelle. The extinction efficiency of such monolayfer and bilayer shell aggregates is thoroughly investigated with appropriate simulations using the Descrete Dipole Approximation (DDA) method. This technique can deal with any arbitrary size, shape, synthesis and external environment. The effect of parameters such as the overall radius of the nanostructure, the small nanosphere radius, and the distance between the nanospheres, on the extinction efficiency factor of the nanostructures was examined. The results indicate that the extinction spectra appear to depend heavily on the distance between the small nanospheres. Finally, the monolayer shell aggregate could be a suitable candidate for use in various biological, intracellular, applications since it provides a reasonably tunable plasmon resonance wavelength while the small size of its components can be exploited for intracellular distribution.

This manuscript presents a novel technique for axial resolution improvement in Fourier Domain Optical Coherence Tomography (FDOCT). The technique is based on the modulated deconvolution of OCT signals which results in a resolution improvement by a factor of ~ 7 without the need for a broader bandwidth light source. This method relies on a combination of two basic properties: beating, which appears when adding two signals of slightly different frequencies, and the resolution improvement, achieved by deconvolution of an OCT image with the encoded source autocorrelation function. In FDOCT the real part of the FFT of the interferogram is modulated by a frequency which depends on the shift in position of the interferogram. A slightly shifted interferogram will, therefore, result in an A-Scan which will have a different modulation frequency. Beating will appear when two such A-Scans, with an appropriately selected amount of shift, are added. Deconvolution of the resulting signal, using suitable kernels, results in a narrower resolution width.

Urinary tract infection diagnosis and antibiogram require a 48 hour waiting period using conventional methods. This results in ineffective treatments, increased costs and most importantly in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra of three species of gram negative Enterobacteria, most commonly responsible for urinary tract infections, were collected. The study included 25 samples each of E.coli, Klebsiella p. and Proteus spp. A novel algorithm based on spectral ratios followed by discriminant analysis resulted in classification with over 94% accuracy. Sensitivity and specificity for the three types of bacteria ranged from 88-100%. For the development of an antibiogram, bacterial samples were treated with the antibiotic ciprofloxacin to which they were all sensitive. Sensitivity to the antibiotic was evident after analysis of the Raman signatures of bacteria treated or not treated with this antibiotic as early as two hours after exposure. This technique can lead to the development of new technology for urinary tract infection diagnosis and antibiogram with same day results, bypassing urine cultures and avoiding all undesirable consequences of current practice.