Investigation of the source‐detector separation in near infrared spectroscopy for healthy and clinical applications

Abstract

Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light‐tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal‐fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source‐detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.

Investigating Developmental Changes in Scalp-Cortex Correspondence Using Diffuse Optical Tomography Sensitivity in Infancy

“…Increasing the separation distance allows the fluence distribution to extend deeper into the head tissues and thus sample cortical regions at greater depth (Mansouri et al, 2010;Strangman et al, 2013;Strangman et al, 2014;Wang et al, 2019). However, the increased depth sensitivity is at the expense of decreased signal strength (Wang et al, 2019). Our results with the depth sensitivity profiles could be used to find similar depth sensitivity with differing source-detector separations for participants at different ages.…”

“…DOT sensitivity is quantified as each location at which a photon traverses through a given tissue region, or "partial path length" (PPL; Mansouri et al, 2010;Strangman, Zhang, & Li, 2014;Wang, Ayaz, & Izzetoglu, 2019). PPL represent the penetration depths of the photon measured in the medium (Mansouri et al, 2010).…”

“…The DOT sensitivity changes as a function of source-detector separation distances (Mansouri et al, 2010;Strangman et al, 2013;Strangman et al, 2014;Wang et al, 2019). As the source-detector distance increases, the fluence distribution covers wider tissue regions, extends deeper into the brain, and decreases in DOT sensitivity.…”

“…For example, it has been found that for 20 mm source-detector separation the fluence distribution was largely confined to shallow layers of the head model and about 6% of the total sensitivity was attributed to the brain (gray and white matters) (Strangman et al, 2013;Strangman et al, 2014). Sensitivity to the brain increased linearly from 20 mm up to 45 mm separation (Mansouri et al, 2010;Strangman et al, 2013;Strangman et al, 2014;Wang et al, 2019). The increase in sensitivity to the brain was less steep at larger distances (Strangman et al, 2013;Strangman et al, 2014).…”

“…It is therefore a common practice to use shorter separations for infants compare to adults (Gervain et al, 2011;Pinti et al, 2020). Increasing the separation distance allows the fluence distribution to extend deeper into the head tissues and thus sample cortical regions at greater depth (Mansouri et al, 2010;Strangman et al, 2013;Strangman et al, 2014;Wang et al, 2019). However, the increased depth sensitivity is at the expense of decreased signal strength (Wang et al, 2019).…”