Machine learning and the Gegenbauer Kernel improve mapping of sub-diffuse optical properties in the spatial frequency domain

Analyzing Spatial Frequency Domain Images (SFDI) of tissue in the sub-diffuse domain can reveal optical properties (μs’, γ) of the tissue related to its microstructural composition and shows potential for use in image-guided cancer removal. However, the determination of sub-diffuse optical properties is currently too slow for real-time applications. Recent research has demonstrated the real-time determination of these properties from experimental measurements using machine learning models, but the γ range of these models falls short of the full spectrum of γ values seen in biological tissue, limited by the range of the simulated datasets used to train these models. The Gegenbauer Kernel has previously been employed in SFDI simulations and been show to allow for simulations across an expanded γ range. Models trained on these simulations have shown success in simulation. We present a novel method which translates γ into analogous parameters of the Gegenbauer Kernel and uses this kernel to simulate datasets over an expanded range of γ values. We train a machine learning model on these datasets and use it to render sub-diffuse optical property heat maps from experimental data of tissue-simulating phantoms and ex vivo skin surgical samples across a full range of values in real-time. We compare this method against the current non-linear fit method and show a significant increase in speed with comparable accuracy. These findings enable real-time rendering of sub-diffuse SFDI for potential use within an image-guided surgery system.