A pixel dependent Finite Element model for spatial frequency domain imaging using NIRFAST
Research output: Contribution to journal › Article › peer-review
Spatial frequency domain imaging (SFDI) utilizes the projection of spatially modulated light pat-terns upon biological tissues to obtain optical property maps for absorption and reduced scatter-ing. Conventionally, both forward modelling and optical property recovery are performed using pixel-independent models, calculated via analytical solutions or Monte-Carlo based look up ta-bles, both assuming a homogenous medium. The resulting recovered maps are limited for sam-ples of high heterogeneity where the homogenous assumption is not valid. NIRFAST, a FEM based image modelling and reconstruction tool, simulates complex heteroge-neous tissue optical interactions for single and multiwavelength systems. Based on the diffusion equation, NIRFAST has been adapted to perform pixel-dependent forward modelling for SFDI. Validation is performed within the spatially resolved domain along with homogenous struc-tured illumination simulations, with a recovery error of <2%. Heterogeneity is introduced through cylindrical anomalies, varying size, depth and optical property values, with recovery errors of <10% as observed across a variety of simulations. This work demonstrates the im-portance of pixel-dependent light interaction modelling for SFDI and its role in quantitative ac-curacy. Here, a full raw image SFDI modelling tool is presented for heterogeneous samples, providing a mechanism towards a pixel dependent SFDI image modelling and parameter recov-ery system.
|Number of pages||15|
|Publication status||Published - 2 Aug 2021|
- SFDI, NIRFAST, FEM, modeling, image reconstruction