A pixel dependent Finite Element model for spatial frequency domain imaging using NIRFAST

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A pixel dependent Finite Element model for spatial frequency domain imaging using NIRFAST. / Mellors, Ben; Dehghani, Hamid.

In: Photonics, Vol. 8, No. 8, 310, 02.08.2021.

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@article{b46b92cb91904913a2d2a659034deb45,
title = "A pixel dependent Finite Element model for spatial frequency domain imaging using NIRFAST",
abstract = "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.",
keywords = "SFDI, NIRFAST, FEM, modeling, image reconstruction",
author = "Ben Mellors and Hamid Dehghani",
year = "2021",
month = aug,
day = "2",
doi = "10.3390/photonics8080310",
language = "English",
volume = "8",
journal = "Photonics",
number = "8",

}

RIS

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 - SFDI

KW - NIRFAST

KW - FEM

KW - modeling

KW - image reconstruction

U2 - 10.3390/photonics8080310

DO - 10.3390/photonics8080310

M3 - Article

VL - 8

JO - Photonics

JF - Photonics

IS - 8

M1 - 310

ER -