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Optical spectroscopy of human tissue has been widely applied within the field of biomedical optics to allow rapid, in vivo characterization and analysis of the tissue. When designing an instrument of this type, an imaging spectrometer is often employed to allow for simultaneous analysis of distinct signals. This is especially important when performing spatially resolved diffuse reflectance spectroscopy. In this article, an algorithm is presented that allows for the automated processing of 2-dimensional images acquired from an imaging spectrometer. The algorithm automatically defines distinct spectrometer tracks and adaptively compensates for distortion introduced by optical components in the imaging chain. Crosstalk resulting from the overlap of adjacent spectrometer tracks in the image is detected and subtracted from each signal. The algorithm's performance is demonstrated in the processing of spatially resolved diffuse reflectance spectra recovered from an Intralipid and ink liquid phantom and is shown to increase the range of wavelengths over which usable data can be recovered.

Optical biopsy techniques offer a minimally invasive, real-time alternative to traditional biopsy and pathology during tumor resection surgery. Diffuse reflectance spectroscopy (DRS) is a commonly used technique in optical biopsy. Optical property recovery from spatially resolved DRS data allows quantification of the scattering and absorption properties of tissue. Monte Carlo simulation methods were used to evaluate a unique fiber-optic probe design for a DRS instrument to be used specifically for optical biopsy of the brain. The probe diameter was kept to a minimum to allow usage in small surgical cavities at least 1 cm in diameter. Simulations showed that the close proximity of fibers to the edge of the probe resulted in boundary effects due to reflection of photons from the surrounding air-tissue interface. A new algorithm for rapid optical property recovery was developed that accounts for this reflection and therefore overcomes these effects. The parameters of the algorithm were adjusted for use over the wide range of optical properties encountered in brain tissue, and its precision was evaluated by subjecting it to random noise. This algorithm can be adapted to work with any probe geometry to allow optical property recovery in small surgical cavities.

Purpose: A 2D∕3D registration algorithm is proposed for registering orthogonal x-ray images with a diagnostic CT volume for high dose rate (HDR) lung brachytherapy., Methods: The algorithm utilizes a rigid registration model based on a pixel∕voxel intensity matching approach. To achieve accurate registration, a robust similarity measure combining normalized mutual information, image gradient, and intensity difference was developed. The algorithm was validated using a simple body and anthropomorphic phantoms. Transfer catheters were placed inside the phantoms to simulate the unique image features observed during treatment. The algorithm sensitivity to various degrees of initial misregistration and to the presence of foreign objects, such as ECG leads, was evaluated., Results: The mean registration error was 2.2 and 1.9 mm for the simple body and anthropomorphic phantoms, respectively. The error was comparable to the interoperator catheter digitization error of 1.6 mm. Preliminary analysis of data acquired from four patients indicated a mean registration error of 4.2 mm., Conclusions: Results obtained using the proposed algorithm are clinically acceptable especially considering the complications normally encountered when imaging during lung HDR brachytherapy.

A planar imaging approach is described for the in vivo quantitative reconstruction of fluorescent point sources in small animals. The method uses the diffusion approximation as a forward model of light propagation from a point source in a homogeneous tissue to find source depth and strength. The tissue optical properties obtained from video reflectometry measurements were used to compensate for the effects of tissue heterogeneity. The method was evaluated on images of fluorescent sources implanted 2-8.5 mm deep in the thigh and abdomen of rats post mortem. In more than 70% of the total number of implants the source depth was retrieved with an error of less than 1 mm. The largest absolute error was 1.9 mm. In retrieving source strength, the errors ranged from 0.4% to 89% generally increasing with increased source depth.

Four models, standard diffusion approximation (SDA), single Monte Carlo (SMC), delta-P1, and isotropic similarity (ISM), are developed and evaluated as forward calculation tools in the estimation of tissue optical properties. The inverse calculation uses the ratio of the fluences and phase difference at two locations close to an intensity modulated isotropic source to recover the reduced scattering coefficient mus' and the absorption coefficient mua. Diffusion theory allows recovery of optical properties (OPs) within 5% for media with mus'mua>10. The performance of the delta-P1 model is similar to SDA, with limited enhanced accuracy. The collimation approximation may limit the use of the delta-P1 model for spherical geometry, and/or the fluence may not be accurately calculated by this model. The SMC model is the best, recovering OPs within 10% regardless of the albedo. However, the necessary restriction of the searched OPs space is inconvenient. The performance of ISM is similar to that of diffusion theory for media with mus'mua>10, and better for 15.

Steady-state diffusion theory models of fluorescence in tissue have been investigated for recovering fluorophore concentrations and fluorescence quantum yield. Spatially resolved fluorescence, excitation and emission reflectance Carlo simulations, and measured using a multi-fibre probe on tissue-simulating phantoms containing either aluminium phthalocyanine tetrasulfonate (AlPcS4), Photofrin meso-tetra-(4-sulfonatophenyl)-porphine dihydrochloride The accuracy of the fluorophore concentration and fluorescence quantum yield recovered by three different models of spatially resolved fluorescence were compared. The models were based on: (a) weighted difference of the excitation and emission reflectance, (b) fluorescence due to a point excitation source or (c) fluorescence due to a pencil beam excitation source. When literature values for the fluorescence quantum yield were used for each of the fluorophores, the fluorophore absorption coefficient (and hence concentration) at the excitation wavelength (mu(a,x,f)) was recovered with a root-mean-square accuracy of 11.4% using the point source model of fluorescence and 8.0% using the more complicated pencil beam excitation model. The accuracy was calculated over a broad range of optical properties and fluorophore concentrations. The weighted difference of reflectance model performed poorly, with a root-mean-square error in concentration of about 50%. Monte Carlo simulations suggest that there are some situations where the weighted difference of reflectance is as accurate as the other two models, although this was not confirmed experimentally. Estimates of the fluorescence quantum yield in multiple scattering media were also made by determining mu(a,x,f) independently from the fitted absorption spectrum and applying the various diffusion theory models. The fluorescence quantum yields for AlPcS4 and TPPS4 were calculated to be 0.59 +/- 0.03 and 0.121 +/- 0.001 respectively using the point source model, and 0.63 +/- 0.03 and 0.129 +/- 0.002 using the pencil beam excitation model. These results are consistent with published values.

The possibility of using spatially resolved fluorescence and reflectance measurements to recover tissue optical properties, fluorophore concentration and the thickness of a superficial layer in a two-layer geometry was investigated. A diffusion theory model was used to fit reflectance and fluorescence data generated using Monte Carlo simulations or experimentally obtained using tissue-simulating phantoms. Initial analysis fitting diffusion theory generated data suggested that it should be possible to recover all parameters from a single set of spatially resolved fluorescence and reflectance measurements. However, when Monte Carlo or experimental data were fitted the results were less impressive. Overall, it was shown that there is a strong coupling between interface depth, fluorophore concentration and tissue absorption, especially at larger depths. The recovery of all input parameters from a single set of spatially resolved measurements was limited to interface depths less than 3 mm, which is a reasonable range for measuring fluorophore in skin. When the tissue optical properties and fluorophore concentrations were known, then the interface depth could be monitored with good accuracy in simulated serial measurements. These results may also point to deficiencies in the diffusion theory model that introduce significant errors in the fitted results.