Stephen Chad Kanick is the Data Science Lead for Profusa Inc., a startup that develops biocompatible subcutaneous biosensors that continuously monitor tissue analytes. Previously, he was an Assistant Professor of Engineering in the Thayer School of Engineering at Dartmouth College, where he still currently holds an adjunct appointment. He completed a post-doctoral appointment in the Center for Optical Diagnostics and Therapy at the Erasmus Medical Center in Rotterdam, the Netherlands. He holds a B.S. degree in Chemical Engineering from West Virginia University, and both M.S. and Ph.D. degrees in Chemical Engineering from the University of Pittsburgh. Chad’s research focuses on the development of new quantitative spectroscopy approaches that are used for diagnosing pathologies, guiding surgeries, and monitoring administered therapies. Chad has authored 50 peer-reviewed publications and has received a Career Development Award (K25) from the National Cancer Institute.
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HSF and CP imaging methods are both known to alter the reflectance image sensitivity to diffuse multiply- scattered and superficially backscattered photons. This results in enhanced contrast, compared to standard wide-field imaging, based on tissue surface microstructure and composition. Measurements in tissue-simulating optical phantoms show that CP images display contrast based on both scattering and absorption, while HSF is specifically sensitive to scatter-only contrast, strongly suppressing absorption-based contrast. By altering the frequency used, the degree of contrast suppression or enhancement can be tuned.1 This suggests that an inexpensive HSF imaging system could have potential to aid diagnostic procedures, where CP is the current state-of-the-art imaging modality.
Monte Carlo modeling is widely used in biomedical optics to describe light transport in complicated situations where closed-form solutions to analytical models do not exist. While this standard definition describes Monte Carlo modeling as powerful and flexible, which it is, it also sounds overly-complicated, which it is not! This course will provide an introduction into both the theoretical concepts and real-world applications of Monte Carlo modeling of light transport in tissue.
The course will provide an interactive description of how the stochastic sampling methods can be used to simulate individual photon-tissue interactions during photon propagation. Attendees will be also be given experience using basic Monte Carlo models and examples will highlight how to develop simulations that accurately mimic experimental measurements. This course would be instructive for anyone who is interested in using Monte Carlo models to guide design choices for new optical measurement approaches.
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