Dynamic phantom with two stage-driven absorbers for mimicking hemoglobin changes in superficial and deep tissues

Area of Science:

• Biomedical Engineering
• Neuroscience
• Optical Imaging

Background:

• Near-infrared spectroscopy (NIRS) is crucial for monitoring brain activity and functional connectivity.
• The influence of superficial tissues (e.g., scalp) on NIRS signals complicates accurate brain activity measurement.
• Existing methods for separating superficial and brain signals lack direct experimental validation due to unknown actual absorption changes.

Purpose of the Study:

• To develop and validate a dynamic phantom for experimentally assessing methods used in NIRS signal analysis.
• To enable reproducible testing of techniques designed to discriminate between superficial tissue absorption and brain activity.
• To provide a tool for improving the accuracy of NIRS-based brain monitoring.

Main Methods:

• Development of a dynamic phantom with two independently controlled absorber layers mimicking hemoglobin changes in superficial and deep tissues.
• Utilizing the phantom to generate reproducible absorption change waveforms simulating brain activity and systemic fluctuations.
• Experimental validation using multiple source-detector distances and applying a subtraction method with short-distance regressors and independent component analysis.

Main Results:

• The dynamic phantom successfully mimicked hemoglobin changes in both superficial and deep tissue layers.
• The shortest-distance channel in multi-distance measurements yielded the most accurate detection of lower-layer (brain) absorption changes.
• Independent component analysis applied to phantom data showed strong agreement with the actual simulated signals.

Conclusions:

• The developed dynamic phantom serves as an effective tool for evaluating and validating NIRS methods aimed at discriminating superficial tissue effects.
• This phantom facilitates the experimental assessment of signal processing techniques for NIRS brain monitoring.
• The findings support the use of advanced signal processing methods, like ICA, in conjunction with multi-distance NIRS for improved brain activity detection.