scholarly journals Speckle illumination spatial frequency domain imaging for projector-free optical property mapping

2021 ◽  
Author(s):  
Mason Chen ◽  
Melina Papadakis ◽  
Nicholas Durr
Keyword(s):  
Optical Property ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Spatial Frequency Domain Imaging ◽  
Spatial Frequency Domain

scholarly journals Optical property uncertainty estimates for spatial frequency domain imaging

2018 ◽  
Vol 9 (2) ◽  
pp. 661 ◽  
Author(s):  
Vivian Pera ◽  
Kavon Karrobi ◽  
Syeda Tabassum ◽  
Fei Teng ◽  
Darren Roblyer
Keyword(s):  
Optical Property ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Spatial Frequency Domain Imaging ◽  
Uncertainty Estimates ◽  
Spatial Frequency Domain

scholarly journals Motion correction in spatial frequency domain imaging; optical property determination in pigmented lesions

2011 ◽  
Author(s):  
John Quan Nguyen ◽  
Rolf B. Saager ◽  
David J. Cuccia ◽  
Kristen M. Kelly ◽  
David Hsiang ◽  
...  
Keyword(s):  
Optical Property ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Motion Correction ◽  
Pigmented Lesions ◽  
Spatial Frequency Domain Imaging ◽  
Spatial Frequency Domain

scholarly journals A Pixel-Dependent Finite Element Model for Spatial Frequency Domain Imaging Using NIRFAST

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 310
Author(s):  
Ben O. L. Mellors ◽  
Hamid Dehghani
Keyword(s):  
Optical Property ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Element Model ◽  
Forward Modeling ◽  
Biological Tissues ◽  
Image Modeling ◽  
Structured Illumination ◽  
Spatial Frequency Domain Imaging ◽  
Spatial Frequency Domain

Spatial frequency domain imaging (SFDI) utilizes the projection of spatially modulated light patterns upon biological tissues to obtain optical property maps for absorption and reduced scattering. Conventionally, both forward modeling and optical property recovery are performed using pixel-independent models, calculated via analytical solutions or Monte-Carlo-based look-up tables, both assuming a homogenous medium. The resulting recovered maps are limited for samples of high heterogeneity, where the homogenous assumption is not valid. NIRFAST, a FEM-based image modeling and reconstruction tool, simulates complex heterogeneous tissue optical interactions for single and multiwavelength systems. Based on the diffusion equation, NIRFAST has been adapted to perform pixel-dependent forward modeling for SFDI. Validation is performed within the spatially resolved domain, along with homogenous structured 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 importance of pixel-dependent light interaction modeling for SFDI and its role in quantitative accuracy. Here, a full raw image SFDI modeling tool is presented for heterogeneous samples, providing a mechanism towards a pixel-dependent SFDI image modeling and parameter recovery system.

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