|
The onset and progression of cancer introduces changes to the intra-cellular ultrastructural components and to the morphology of the extracellular matrix. While previous work has shown that localized scatter imaging is sensitive to pathology-induced differences in these aspects of tissue microstructure, wide adaptation this knowledge for surgical guidance is limited by two factors. First, the time required to image with confocal-level localization of the remission signal can be substantial. Second, localized (i.e. sub-diffuse) scatter remission intensity is influenced interchangeably by parameters that define scattering frequency and anisotropy. This similarity relationship must be carefully considered in order to obtain unique estimates of biomarkers that define either the scatter density or features that describe the distribution (e.g. shape, size, and orientation) of scatterers. This study presents a novel approach that uses structured light imaging to address both of these limitations.
Monte Carlo data were used to model the reflectance intensity over a wide range of spatial frequencies, reduced scattering coefficients, absorption coefficients, and a metric of the scattering phase function that directly maps to the fractal dimension of scatter sizes. The approach is validated in tissue-simulating phantoms constructed with user-tuned scattering phase functions. The validation analysis shows that the phase function can be described in the presence of different scatter densities or background absorptions. Preliminary data from clinical tissue specimens show quantitative images of both the scatter density and the tissue fractal dimension for various tissue types and pathologies. These data represent a novel wide-field quantitative approach to mapping microscopic structural biomarkers that cannot be obtained with standard diffuse imaging. Implications for the use of this approach to assess surgical margins will be discussed.
|
