Kernel Flow: A high channel count scalable TD-fNIRS system

Han Y. Ban; Geoffrey M. Barrett; Alex Borisevich; Ashutosh Chaturvedi; Jacob L. Dahle; Hamid Dehghani; Bruno DoValle; Julien Dubois; Ryan M. Field; Viswanath Gopalakrishnan; Andrew Gundran; Michael Henninger; Wilson C. Ho; Howard D. Hughes; Rong Jin; Julian Kates-Harbeck; Thanh Landy; Antonio H. Lara; Michael Leggiero; Gabriel Lerner; Zahra M. Aghajan; Michael Moon; Alejandro Ojeda; Isai Olvera; Meric Ozturk; Sangyong Park; Milin J. Patel; Katherine L. Perdue; Wing Poon; Zachary P. Sheldon; Benjamin Siepser; Sebastian Sorgenfrei; Nathan Sun; Victor Szczepanski; Mary Zhang; Zhenye Zhu

doi:10.1117/12.2582888

Kernel Flow: A high channel count scalable TD-fNIRS system

Han Y. Ban, Geoffrey M. Barrett, Alex Borisevich, Ashutosh Chaturvedi, Jacob L. Dahle, Hamid Dehghani, Bruno DoValle, Julien Dubois, Ryan M. Field^*, Viswanath Gopalakrishnan, Andrew Gundran, Michael Henninger, Wilson C. Ho, Howard D. Hughes, Rong Jin, Julian Kates-Harbeck, Thanh Landy, Antonio H. Lara, Michael Leggiero, Gabriel LernerZahra M. Aghajan, Michael Moon, Alejandro Ojeda, Isai Olvera, Meric Ozturk, Sangyong Park, Milin J. Patel, Katherine L. Perdue, Wing Poon, Zachary P. Sheldon, Benjamin Siepser, Sebastian Sorgenfrei, Nathan Sun, Victor Szczepanski, Mary Zhang, Zhenye Zhu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Time-Domain Near-Infrared Spectroscopy (TD-NIRS) has been considered as the gold standard of non-invasive 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 (CW) NIRS systems. Kernel Flow is a TD-NIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-NIRS system while integrating all of the components into a small modular device. 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. We show performance similar to benchtop systems with our miniaturized device.

Original language	English
Title of host publication	Integrated Sensors for Biological and Neural Sensing
Editors	Hooman Mohseni
Publisher	SPIE
ISBN (Electronic)	9781510641617
DOIs	https://doi.org/10.1117/12.2582888
Publication status	Published - 5 Mar 2021
Event	Integrated Sensors for Biological and Neural Sensing 2021 - Virtual, Online, United States Duration: 6 Mar 2021 → 11 Mar 2021

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	11663
ISSN (Print)	1605-7422

Conference

Conference	Integrated Sensors for Biological and Neural Sensing 2021
Country/Territory	United States
City	Virtual, Online
Period	6/03/21 → 11/03/21

Bibliographical note

Funding Information:
We would like to thank Frédéric Lange and Ilias Tachtsidis for their advice on the measurement protocols, Husam Katnani for his system design guidance, Katriece Ray for her design support, and Bryan Johnson for his product vision and helpful discussions. This work was supported by Kernel.

Publisher Copyright:
© 2021 SPIE. All rights reserved.

Keywords

fNIRS
Imaging systems
MEDPHOT
Optical brain imaging
Optical properties
Single photon detectors
Time resolved spectroscopy
Tissue optics

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Biomaterials
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2582888Licence: None: All rights reserved

Cite this

Ban, H. Y., Barrett, G. M., Borisevich, A., Chaturvedi, A., Dahle, J. L., Dehghani, H., DoValle, B., Dubois, J., Field, R. M., Gopalakrishnan, V., Gundran, A., Henninger, M., Ho, W. C., Hughes, H. D., Jin, R., Kates-Harbeck, J., Landy, T., Lara, A. H., Leggiero, M., ... Zhu, Z. (2021). Kernel Flow: A high channel count scalable TD-fNIRS system. In H. Mohseni (Ed.), Integrated Sensors for Biological and Neural Sensing Article 116630B (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11663). SPIE. https://doi.org/10.1117/12.2582888

@inproceedings{93862ea4c88d46a9988e1fd203c7d0dc,

title = "Kernel Flow: A high channel count scalable TD-fNIRS system",

abstract = "Time-Domain Near-Infrared Spectroscopy (TD-NIRS) has been considered as the gold standard of non-invasive 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 (CW) NIRS systems. Kernel Flow is a TD-NIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-NIRS system while integrating all of the components into a small modular device. 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. We show performance similar to benchtop systems with our miniaturized device.",

keywords = "fNIRS, Imaging systems, MEDPHOT, Optical brain imaging, Optical properties, Single photon detectors, Time resolved spectroscopy, Tissue optics",

author = "Ban, {Han Y.} and Barrett, {Geoffrey M.} and Alex Borisevich and Ashutosh Chaturvedi and Dahle, {Jacob L.} and Hamid Dehghani and Bruno DoValle and Julien Dubois and Field, {Ryan M.} and Viswanath Gopalakrishnan and Andrew Gundran and Michael Henninger and Ho, {Wilson C.} and Hughes, {Howard D.} and Rong Jin and Julian Kates-Harbeck and Thanh Landy and Lara, {Antonio H.} and Michael Leggiero and Gabriel Lerner and Aghajan, {Zahra M.} and Michael Moon and Alejandro Ojeda and Isai Olvera and Meric Ozturk and Sangyong Park and Patel, {Milin J.} and Perdue, {Katherine L.} and Wing Poon and Sheldon, {Zachary P.} and Benjamin Siepser and Sebastian Sorgenfrei and Nathan Sun and Victor Szczepanski and Mary Zhang and Zhenye Zhu",

note = "Funding Information: We would like to thank Fr{\'e}d{\'e}ric Lange and Ilias Tachtsidis for their advice on the measurement protocols, Husam Katnani for his system design guidance, Katriece Ray for her design support, and Bryan Johnson for his product vision and helpful discussions. This work was supported by Kernel. Publisher Copyright: {\textcopyright} 2021 SPIE. All rights reserved.; Integrated Sensors for Biological and Neural Sensing 2021 ; Conference date: 06-03-2021 Through 11-03-2021",

year = "2021",

month = mar,

day = "5",

doi = "10.1117/12.2582888",

language = "English",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Hooman Mohseni",

booktitle = "Integrated Sensors for Biological and Neural Sensing",

}

Ban, HY, Barrett, GM, Borisevich, A, Chaturvedi, A, Dahle, JL, Dehghani, H, DoValle, B, Dubois, J, Field, RM, Gopalakrishnan, V, Gundran, A, Henninger, M, Ho, WC, Hughes, HD, Jin, R, Kates-Harbeck, J, Landy, T, Lara, AH, Leggiero, M, Lerner, G, Aghajan, ZM, Moon, M, Ojeda, A, Olvera, I, Ozturk, M, Park, S, Patel, MJ, Perdue, KL, Poon, W, Sheldon, ZP, Siepser, B, Sorgenfrei, S, Sun, N, Szczepanski, V, Zhang, M & Zhu, Z 2021, Kernel Flow: A high channel count scalable TD-fNIRS system. in H Mohseni (ed.), Integrated Sensors for Biological and Neural Sensing., 116630B, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11663, SPIE, Integrated Sensors for Biological and Neural Sensing 2021, Virtual, Online, United States, 6/03/21. https://doi.org/10.1117/12.2582888

Kernel Flow: A high channel count scalable TD-fNIRS system. / Ban, Han Y.; Barrett, Geoffrey M.; Borisevich, Alex et al.
Integrated Sensors for Biological and Neural Sensing. ed. / Hooman Mohseni. SPIE, 2021. 116630B (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11663).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Kernel Flow

T2 - Integrated Sensors for Biological and Neural Sensing 2021

AU - Ban, Han Y.

AU - Barrett, Geoffrey M.

AU - Borisevich, Alex

AU - Chaturvedi, Ashutosh

AU - Dahle, Jacob L.

AU - Dehghani, Hamid

AU - DoValle, Bruno

AU - Dubois, Julien

AU - Field, Ryan M.

AU - Gopalakrishnan, Viswanath

AU - Gundran, Andrew

AU - Henninger, Michael

AU - Ho, Wilson C.

AU - Hughes, Howard D.

AU - Jin, Rong

AU - Kates-Harbeck, Julian

AU - Landy, Thanh

AU - Lara, Antonio H.

AU - Leggiero, Michael

AU - Lerner, Gabriel

AU - Aghajan, Zahra M.

AU - Moon, Michael

AU - Ojeda, Alejandro

AU - Olvera, Isai

AU - Ozturk, Meric

AU - Park, Sangyong

AU - Patel, Milin J.

AU - Perdue, Katherine L.

AU - Poon, Wing

AU - Sheldon, Zachary P.

AU - Siepser, Benjamin

AU - Sorgenfrei, Sebastian

AU - Sun, Nathan

AU - Szczepanski, Victor

AU - Zhang, Mary

AU - Zhu, Zhenye

N1 - Funding Information: We would like to thank Frédéric Lange and Ilias Tachtsidis for their advice on the measurement protocols, Husam Katnani for his system design guidance, Katriece Ray for her design support, and Bryan Johnson for his product vision and helpful discussions. This work was supported by Kernel. Publisher Copyright: © 2021 SPIE. All rights reserved.

PY - 2021/3/5

Y1 - 2021/3/5

N2 - Time-Domain Near-Infrared Spectroscopy (TD-NIRS) has been considered as the gold standard of non-invasive 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 (CW) NIRS systems. Kernel Flow is a TD-NIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-NIRS system while integrating all of the components into a small modular device. 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. We show performance similar to benchtop systems with our miniaturized device.

AB - Time-Domain Near-Infrared Spectroscopy (TD-NIRS) has been considered as the gold standard of non-invasive 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 (CW) NIRS systems. Kernel Flow is a TD-NIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-NIRS system while integrating all of the components into a small modular device. 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. We show performance similar to benchtop systems with our miniaturized device.

KW - fNIRS

KW - Imaging systems

KW - MEDPHOT

KW - Optical brain imaging

KW - Optical properties

KW - Single photon detectors

KW - Time resolved spectroscopy

KW - Tissue optics

UR - http://www.scopus.com/inward/record.url?scp=85108458000&partnerID=8YFLogxK

U2 - 10.1117/12.2582888

DO - 10.1117/12.2582888

M3 - Conference contribution

AN - SCOPUS:85108458000

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Integrated Sensors for Biological and Neural Sensing

A2 - Mohseni, Hooman

PB - SPIE

Y2 - 6 March 2021 through 11 March 2021

ER -

Kernel Flow: A high channel count scalable TD-fNIRS system

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this