1. Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system
- Author
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Han Y. Ban, Geoffrey M. Barrett, Alex Borisevich, Ashutosh Chaturvedi, Jacob L. Dahle, Hamid Dehghani, Julien Dubois, Ryan M. Field, Viswanath Gopalakrishnan, Andrew Gundran, Michael Henninger, Wilson C. Ho, Howard D. Hughes, Rong Jin, Julian Kates-Harbeck, Thanh Landy, Michael Leggiero, Gabriel Lerner, Zahra M. Aghajan, Michael Moon, Isai Olvera, Sangyong Park, Milin J. Patel, Katherine L. Perdue, Benjamin Siepser, Sebastian Sorgenfrei, Nathan Sun, Victor Szczepanski, Mary Zhang, and Zhenye Zhu
- Subjects
Paper ,optical properties ,Spectroscopy, Near-Infrared ,optical brain imaging ,Biomedical Engineering ,Brain ,single-photon detectors ,time-resolved spectroscopy ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Biomaterials ,Special Section on Tissue Phantoms to Advance Biomedical Optical Systems ,functional near-infrared spectroscopy ,Humans ,tissue optics - Abstract
Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.
- Published
- 2022
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