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Optode geometry plays an important role in achieving both good spatial resolution and spatial uniformity of detection in diffuse-optical-imaging-based brain activation studies. The quality of reconstructed images for six optode geometries were studied and compared using a laboratory tissue phantom model that contained an embedded object at two separate locations. The number of overlapping measurements per pixel (i.e., the measurement density) and their spatial distributions were quantified for all six geometries and were correlated with the quality of the resulting reconstructed images. The latter were expressed by the area ratio (AR) and contrast-to-noise ratio (CNR) between reconstructed and actual objects. Our results revealed clearly that AR and CNR depended on the measurement density asymptotically, having an optimal point for measurement density beyond which more overlapping measurements would not significantly improve the quality of reconstructed images. Optimization of probe geometry based on our method demonstrated that a practical compromise can be attained between DOI spatial resolution and measurement density.

Biliary tree structures are embedded in adipose tissue and, therefore, cannot be visualized directly by the surgeon during cholecystectomy operations. This can lead to inadvertent injuries with serious complications for the patient. Computational studies were performed to assess the feasibility of noninvasively localizing these structures from spectrally resolved near-infrared reflectance measurements. Methodologies were developed for vessel localization, both on the adipose tissue surface and depthwise, by use of semi-infinite and two-layer models of diffuse photon propagation in tissues, respectively. The simulation results, along with some preliminary experimental measurements on tissue-simulating phantoms, prove the feasibility of these methods and show promise for their future clinical application., ((c) 2008 Optical Society of America)

The general two-layer inverse problem in biomedical photon migration is to estimate the absorption and scattering coefficients of each layer as well as the top-layer thickness. We attempted to solve this problem, using experimental and simulated spatially resolved frequency-domain (FD) reflectance for optical properties typical of skin overlying muscle or skin overlying fat in the near infrared. Two forward models of light propagation were used: a two-layer diffusion solution [Appl. Opt. 37, 779 (1998)] and a hybrid Monte Carlo (MC) diffusion model [Appl. Opt. 37, 7401 (1998)]. MC-simulated FD reflectance data were fitted as relative measurements to the hybrid and the pure diffusion models. It was found that the hybrid model could determine all the optical properties of the two-layer media studied to ~5%. Also, the same accuracy could be achieved by means of fitting MC-simulated cw reflectance data as absolute measurements, but fitting them as relative ones is an ill-posed problem. In contrast, two-layer diffusion could not retrieve the top-layer optical properties as accurately for FD data and was ill-posed for both relative and absolute cw data. The hybrid and the pure diffusion models were also fitted to experimental FD reflectance measurements from two-layer tissue-simulating phantoms representative of skin-on-fat and skin-on-muscle baseline optical properties. Both the hybrid and the diffusion models could determine the optical properties of the lower layer. The hybrid model demonstrated its potential to retrieve quantitatively the transport scattering coefficient of skin (the upper layer), which was not possible with the pure diffusion model. Systematic discrepancies between model and experiment may compromise the accuracy of the deduced top-layer optical properties. Identifying and eliminating such discrepancies is critical to practical application of the method.

We propose a hybrid Monte Carlo (MC) diffusion model for calculating the spatially resolved reflectance amplitude and phase delay resulting from an intensity-modulated pencil beam vertically incident on a two-layer turbid medium. The model combines the accuracy of MC at radial distances near the incident beam with the computational efficiency afforded by a diffusion calculation at further distances. This results in a single forward calculation several hundred times faster than pure MC, depending primarily on model parameters. Model predictions are compared with MC data for two cases that span the extremes of physiologically relevant optical properties: skin overlying fat and skin overlying muscle, both in the presence of an exogenous absorber. It is shown that good agreement can be achieved for radial distances from 0.5 to 20 mm in both cases. However, in the skin-on-muscle case the choice of model parameters and the definition of the diffusion coefficient can lead to some interesting discrepancies.

We have examined the possibility of determining the optical properties of a two-layer medium by using a diffusion approximation radiation transport model [Appl. Opt. 37, 779 (1998)]. Continuous-wave and frequency-domain (FD) low-noise Monte Carlo (MC) data were fitted to the model. Marquardt-Levenberg and a simulated annealing algorithm were used and compared as optimization techniques. Our particular choice of optical properties for the two-layer model was consistent with skin and underlying fat in the presence of an exogenous chromophore [Appl. Opt. 37, 1958 (1998)]. The results are therefore specific to this set of optical properties. It was found that the cw diffusion solution could never be used to estimate all optical properties reliably. The combined cw and FD solutions could not be used to estimate some of the top-layer optical properties to an accuracy of better than 10%, although the absorption and the transport scattering coefficients of the bottom layer could be estimated to within 7% and 0.5%, respectively. No improvement was found from simultaneously fitting MC data at three different modulation frequencies. These results point to the need for a more accurate radiation transfer model at small source-detector separations.