Spatial Frequency Domain Imaging (SFDI) is a Diffuse Optical Imaging (DOI) technique that is well suited for preclinical functional imaging. Recently, we have shown that SFDI can successfully be used for longitudinal monitoring of a prostate subcutaneous tumor xenograft, where we have applied a look-up-table (LUT) based approach to extract tissue absorption (μa) and scattering properties (μs’). This LUT assumes a semi-infinite homogeneous medium and simulates reflectance (Rd) in spatial domain, and scales Rd for all μa and μs’ of interest from a single Monte Carlo simulation. However, converting Rd to spatial frequency domain (SFD) and scaling for μs’ may introduces unacceptable errors. Most importantly, the homogeneous model fails to mimic the actual physiology of a subcutaneous tumor, which can be described as a two-layer medium with a thin skin layer above the tumor layer. To overcome these limitations, we have developed a Monte Carlo based two-layer LUT with a wide range of tumor (bottom) layer optical properties, and fixed skin (top) properties. The two-layer LUT will be validated by two-layer silicone phantoms and tested for sensitivity to inaccurate layer assumptions. Additionally, the homogeneous and two-layer LUTs will be used on a large mouse tumor database (n=54 mice monitored over 3 months) to identify how the two-layer LUT can improve accuracy of SFDI by more accurately reflecting in vivo physiology, and reducing discretization and scaling errors. Improved SFDI findings in small animals, in the long run, will help establish clinical DOI tools for early detection of chemotherapy efficacy during treatment.
Syeda Tabassum, Vivian E. Pera, Gage J. Greening, Timothy J. Muldoon, and Darren M. Roblyer, "Long-term longitudinal monitoring of chemotherapy response using Spatial Frequency Domain Imaging using an improved two-layer Monte Carlo based inverse model (Conference Presentation)," Proc. SPIE 10472, Diagnosis and Treatment of Diseases in the Breast and Reproductive System IV, 1047208 (Presented at SPIE BiOS: January 27, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2289293.5751470606001.
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