Gao Yuanyuan, Rogers De'Ja, von Lühmann Alexander, Ortega-Martinez Antonio, Boas David A, Yücel Meryem Ayşe
Boston University, Neurophotonics Center, Boston, Massachusetts, United States.
Neurophotonics. 2023 Apr;10(2):025007. doi: 10.1117/1.NPh.10.2.025007. Epub 2023 May 23.
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.
Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously.
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.
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.
The SS-DOT model improves the fNIRS image reconstruction quality.
短间隔（SS）回归和扩散光学断层扫描（DOT）图像重建是功能近红外光谱（fNIRS）中广泛采用的两种方法，已证明它们各自有助于分离脑激活信号和生理信号，若依次使用这两种方法可进一步改善效果。我们推测同时进行这两种操作会进一步提高性能。
受这两种方法成功的启发，我们提出了一种同时应用SS和DOT的方法，即SS-DOT。
该方法利用空间和时间基函数来表示血红蛋白浓度变化，使我们能够将SS回归因子纳入时间序列DOT模型。为了将SS-DOT模型的性能与传统的顺序模型进行比较，我们使用了通过合成脑反应增强的fNIRS静息状态数据以及在握球任务期间采集的数据。传统的顺序模型包括执行SS回归和DOT。
结果表明，SS-DOT模型通过将对比度与背景比提高三倍来改善图像质量。在小的脑激活情况下，益处不大。
SS-DOT模型提高了fNIRS图像重建质量。
