Dr. Chad C. Kanick
at Lark Technologies Inc
SPIE Involvement:
Author | Instructor | Special Event Speaker
Area of Expertise:
Biomedical Optics , Reflectance and Fluorescence Spectroscopy , Monte Carlo Modeling , Clinical Translation , Data Science , Medical Devices
Profile Summary

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.
Publications (32)

SPIE Journal Paper | 7 May 2024 Open Access
Jeeseong Hwang, Philip Cheney, Stephen Kanick, Hanh N. Le, David McClatchy, Helen Zhang, Nian Liu, Zhan-Qian John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue
JBO, Vol. 29, Issue 09, 093503, (May 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.9.093503
KEYWORDS: Tissues, Biological samples, Image segmentation, Tumors, Breast, Microscopy, Printed circuit board testing, Blood, Library classification systems, Surgery

Proceedings Article | 13 March 2024 Poster
Helen Zhang, Philip Cheney, Stephan Kanick, Hanh Le, David McClatchy, Nian Liu, John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue, Jeeseong Hwang
Proceedings Volume PC12833, PC128330M (2024) https://doi.org/10.1117/12.3023601

SPIE Journal Paper | 30 June 2021 Open Access
JBO, Vol. 26, Issue 06, 062701, (June 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.6.062701
KEYWORDS: Point-of-care devices, Photonics, Biomedical optics, Tissues, Sensors, Tissue optics, Skin, Speckle, Signal to noise ratio, Optical phantoms

SPIE Journal Paper | 27 September 2018 Open Access
David McClatchy, Elizabeth Rizzo, Wendy Wells, Candice Black, Keith Paulsen, Stephen Kanick, Brian Pogue
JBO, Vol. 24, Issue 07, 071605, (September 2018) https://doi.org/10.1117/12.10.1117/1.JBO.24.7.071605
KEYWORDS: Light scattering, Optical properties, Tissue optics, Breast, Optical testing, Tissues, Scattering, Scatter measurement, Cancer, Image segmentation

Proceedings Article | 19 April 2017 Presentation
Proceedings Volume 10049, 1004906 (2017) https://doi.org/10.1117/12.2252589
KEYWORDS: Scattering, Light scattering, Tissue optics, Tissues, Modulation, Reflectivity, Breast, Tumor growth modeling, Statistical analysis, Optical properties

Showing 5 of 32 publications
Course Instructor
SC1152: Monte Carlo Modeling Explained
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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