Towards pixel-dependent spatial frequency domain imaging modelling and recovery

Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. The light interaction theory behind SFDI is based upon homogenous properties, with forward models calculated via analytical solutions or Monte-Carlo, also used for the optical property recovery, using only a pixel-independent nature. This is known to be limited for samples with high heterogeneity, with an increased error observed for varying optical property boundaries. NIRFAST is an image modelling and reconstruction tool based upon FEM of the diffusion model that simulates complex heterogenic tissue interactions from single and multi-wavelength systems and is routinely used in a variety of clinical and pre-clinical applications. NIRFAST has been adapted for SFDI, allowing for pixel-dependent heterogenic simulations. Image reconstruction using existing methodologies is compared to data generated from complex models with NIRFAST to quantify the optical property reconstruction accuracy, whilst heterogenous models of varying optical property values and depths further demonstrate SFDIs parameter recovery capabilities. It is shown that pixel-dependent light interaction in tissue plays an important part of accurate optical map recovery and can affect quantitative accuracy. This work demonstrates full raw image SFDI simulations for heterogenous samples working towards the use of model-based image reconstruction to allow a coupled, pixel-dependent SFDI image modelling and parameter recovery.