TY - JOUR
T1 - A pixel dependent Finite Element model for spatial frequency domain imaging using NIRFAST
AU - Mellors, Ben
AU - Dehghani, Hamid
PY - 2021/8/2
Y1 - 2021/8/2
N2 - 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.
AB - 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.
KW - FEM
KW - NIRFAST
KW - SFDI
KW - image reconstruction
KW - modeling
UR - http://www.scopus.com/inward/record.url?scp=85112134691&partnerID=8YFLogxK
U2 - 10.3390/photonics8080310
DO - 10.3390/photonics8080310
M3 - Article
VL - 8
JO - Photonics
JF - Photonics
IS - 8
M1 - 310
ER -