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Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy
Gao, Yuanyuan Rogers, De’Ja Von Lühmann, Alexander Ortega-Martinez, Alexander Boas, David A. Yücel, Meryem A.
Gao, Yuanyuan
Rogers, De’Ja
Von Lühmann, Alexander
Ortega-Martinez, Alexander
Boas, David A.
Yücel, Meryem A.
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2023-05
Type
Article
Genre
Keywords
High-density functional near-infrared spectroscopy,Diffuse optical tomography,Short-separation regression,Optical image reconstruction
Subjects (LCSH)
Citation
Yuanyuan Gao, De’Ja Rogers, Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, Meryem A. Yücel, "Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy," Neurophoton. 10(2) 025007 (23 May 2023) https://doi.org/10.1117/1.NPh.10.2.025007
Abstract
Significance
Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance.
Aim
Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously.
Approach
The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT.
Results
The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation.
Conclusions
The SS-DOT model improves the fNIRS image reconstruction quality.
Table of Contents
Description
Publisher
SPIE
Journal
Neurophotonics
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2329-423X
2329-4248
2329-4248