Model-Based Design of Optical Diagnostic Instrumentation
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Biophotonics methods are attractive since they allow for the non-invasive diagnosis of cancer. Experiments were carried out to investigate the feasibility of detecting early pre-cancer using optical spectroscopy. However, optimization of instrumentation design parameters remains challenging because of the lack of metrics to evaluate the performance of certain design parameters. For example, although using angled-collection geometry has been shown to collect depth sensitive spatial origins, the performance of devices with angled-collection geometries are not well characterized or quantified. In this study, we use a polarization-sensitive Monte Carlo simulation (Pol-MC) to aid in the design of instrumentation for the early detection of epithelial cancer. The tissue is modeled in layers: (0) air outside the tissue, (1) epithelial layer, (2) thin pre-cancer layer of cells, (3) thin basement membrane, implemented as a thin transparent layer, and (4) the stroma, implemented as a thick layer of scattering material. We propose a new metric, Target Signal Ratio (TSR), to evaluate the proportion of signal that is scattered from a target layer, which is the basal/pre-cancer layer. This study is a proof-of-concept for the application of computational techniques to facilitate instrument design.