Abstract
SIGNIFICANCE: Current paradigms for the optical characterization of layered tissues involve explicit consideration of an inverse problem which is often ill-posed and whose resolution may retain significant uncertainty. Here, we present an alternative approach, structured light imaging mesoscopy (SLIM), that leverages the inherent sensitivity of raw spatial frequency domain (SFD) reflectance measurements for the detection of embedded subsurface scattering changes in tissue. AIM: We identify wavelength-spatial frequency ( λ-fx ) combinations that provide optimal sensitivity of SFD reflectance changes originating from scattering changes in an embedded tissue layer. We specifically consider the effects of scattering changes in the superficial dermis which is a key locus of pathology for diverse skin conditions such as cancer, aging, and scleroderma. APPROACH: We used Monte Carlo simulations in a four-layer skin model to analyze the SFD reflectance changes resulting from changes in superficial dermal scattering across wavelength ( λ = 471 to 851 nm) and spatial frequency ( fx = 0 to 0.5/mm). Within this model, we consider different values for epidermal melanin concentration to simulate variations in skin tone. RESULTS: Monte Carlo simulations revealed that scattering changes within the superficial dermis produce SFD reflectance changes which are maximized at specific ( λ-fx ) pairs and vary with skin tone. For light skin tones, SFD reflectance changes due to scattering reductions in the superficial dermis are maximized at λ = 621 nm and spatial frequency fx ≈ 0.33/mm . By contrast, for darker skin tones, maximal SFD reflectance changes occur at wavelengths in the near-infrared ( λ ≥ 811 nm ) at a spatial frequency of fx ≈ 0.25/mm . Interestingly, the change in SFD reflectance produced by such scattering changes is most uniform across all skin tones when using the longest wavelength tested ( λ = 851 nm ) and a spatial frequency of fx ≈ 0.22/mm . Taken together, our computational model identifies specific ( λ-fx ) pairs to optimally detect embedded structural alterations in the superficial dermis. CONCLUSIONS: The findings establish the SLIM methodology as a means to detect morphological changes in an embedded subsurface tissue layer by leveraging inherent sensitivities of spatial frequency domain reflectance. This approach promises to enable simplified clinical tracking of subsurface microstructural alterations without the explicit need to consider an inverse problem approach.