Significance
Frequency domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS) are used for the label-free measurement of chromophore concentrations, blood flow, and metabolism for tissues such as muscle or tumors. These tissues are embedded under the skin and adipose, the properties of which can vary between subjects, thus affecting the extraction of the target tissue’s optical properties.
Aim
We aim to characterize the effects of the skin tone and adipose thickness on FD-NIRS and DCS measurements and develop subject-specific multi-layer inverse models that account for these effects.
Approach
A three-layer look-up-table-based inverse model that accounted for the skin tone and adipose thickness was generated using Monte Carlo simulations for each subject. Stackable tissue-mimicking silicone phantoms were fabricated and used to validate the models. A custom combined FD-NIRS and DCS system was then used to measure phantoms and the sternocleidomastoid muscle of healthy subjects. Subjects performed a breathing exercise that consisted of a baseline, load, and recovery. The skin tone of subjects was determined using a colorimeter. The adipose thickness was determined using ultrasound. The subject-specific three-layer model results were compared against a simpler single-layer model.
Results
The skin tone and adipose thickness substantially affected the extraction of multiple FD-NIRS and DCS parameters. Oxygenated hemoglobin, total hemoglobin, tissue saturation (StO2), and blood flow index (BFi) values were all underestimated if the skin tone and adipose thickness were not accounted for (all p-values<0.01). For example, StO2 was underestimated by 18±9 %pt (p<0.0001) and BFi was underestimated by 7±8×10−6 mm2/s (p<0.01). Hemodynamics during a respiratory exercise were also underestimated in the case of oxygenated hemoglobin, total hemoglobin, BFi, and metabolic rate of oxygenation (all p<0.05).
Conclusion
We highlight the importance of accounting for both adipose thickness and skin tone when targeting underlying tissue. The multi-layer models we developed have the potential to be applied to a wide range of in vivo studies.
