Abstract
.Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.
Highlights
Concentration of tissue constituents, such as hemoglobin, water, or lipid, provides vital functional information about tissue health, which can assist healthcare practitioners in making important decisions
We propose an alternative method for the inversion using a publicly available machine learning technique to estimate the optical properties maps from diffuse reflectance images applied to spatial frequency domain imaging (SFDI)
Machine learning methods provide a versatile means for replacing nonlinear models to estimate optical properties from diffuse reflectance images in SFDI without compromising accuracy of estimation
Summary
Concentration of tissue constituents, such as hemoglobin, water, or lipid, provides vital functional information about tissue health, which can assist healthcare practitioners in making important decisions. Quantitative information about tissue oxygenation and blood volume fraction can provide visual assistance during surgery and monitoring tissue condition during and after treatment.[1] Even though devices that provide this vital information at a single point already exist, there is a constant push and need toward attaining noncontact, real-time, wide-field and quantitative mapping of tissue functional properties. To address this need, spatial frequency domain imaging (SFDI) is a promising candidate within the field of diffuse optical imaging that has already found commercial application in aiding medical treatment.[2]. Polarizers, arranged in a cross configuration, are commonly used to reduce the effect of specular reflectance and record only diffuse light out of the tissue
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