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.
Adequate tumor margin delineation is crucial to maximize positive patient outcomes in molecular-guided surgery. Raman spectroscopy is highly specific in detecting tumor margins based on the differences in molecular composition between tumor and normal tissue; however, one major technical hurdle to its adoption is its slow acquisition speed. Previously, we described a "superpixel" acquisition approach that can expedite up to 10,000x compared to point-bypoint scanning while covering the entire surface area. We detected human basal cell carcinoma in Mohs surgical resection margins from eight patients and demonstrated superpixel acquisition had consistent diagnostic performance with point-by-point scanning. In this work, we further demonstrated examples of raster-scanned superpixel Raman classification images of positive and negative margins from three new patients. The performance of three superpixel sizes were evaluated, including 25×25μm2, 50×50μm2 and 100×100μm2. A previous established biophysical inverse model was applied to extract the biochemical composition of each superpixel, and a prior classification model was employed to generate the tumor heatmap. The classification result was then compared with the histopathological image. Our results show that superpixel Raman imaging can overcome the limitation of traditional Raman imaging in speed, allowing for rapid tumor margin assessment.