Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density...

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Optical Spectroscopy

Background:

• Functional near-infrared spectroscopy (fNIRS) utilizes short-separation (SS) regression and diffuse optical tomography (DOT) for brain signal analysis.
• Sequential application of SS regression and DOT improves separation of brain activation and physiological signals.

Purpose of the Study:

• To develop and evaluate a simultaneous SS-DOT method for enhanced fNIRS image reconstruction.
• To investigate if simultaneous application of SS and DOT outperforms sequential methods.

Main Methods:

• Proposed the SS-DOT model, integrating SS regressors into a time series DOT framework using spatial and temporal basis functions.
• Benchmarked SS-DOT against conventional sequential SS regression and DOT models using resting-state fNIRS data with synthetic and task-based brain responses.

Main Results:

• The SS-DOT model demonstrated a threefold increase in contrast-to-background ratio, significantly improving image quality.
• Observed marginal benefits for small brain activation levels.

Conclusions:

• The SS-DOT model effectively enhances functional near-infrared spectroscopy (fNIRS) image reconstruction quality.
• Simultaneous integration of SS regression and DOT offers superior performance compared to sequential approaches.