Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse...

Area of Science:

• Biomedical Engineering
• Neuroimaging Science
• Optical Physics

Background:

• Diffuse optical tomography (DOT) for human neuroimaging faces challenges due to head size, geometry, and depth-specific signal detection.
• High-density DOT systems have shown promise, achieving retinotopic measurements comparable to fMRI and PET.
• Advancing DOT neuroimaging requires understanding measurement sensitivity within the complex head geometry.

Purpose of the Study:

• To investigate the impact of source-detector separation on DOT imaging sensitivity within the adult human head.
• To quantify how different data sampling strategies affect the ability to image brain tissue at various depths.
• To optimize high-density DOT array configurations for enhanced neuroimaging capabilities.

Main Methods:

• Utilized numerical simulations with a finite element model of the adult head.
• Analyzed signal sensitivity as a function of imaging array configuration and data sampling.
• Quantified imaging sensitivity at different brain depths based on source-detector separation.

Main Results:

• Second nearest neighbor (NN) measurements in a 1.3 cm grid suffice for imaging superficial cortical gyri (<5 mm depth).
• Fourth and fifth NN measurements are necessary for imaging deeper into cortical sulci (>15 mm depth).
• Increasing maximum source-detector separation enhances sensitivity for deeper brain structures.

Conclusions:

• Source-detector separation is a critical parameter for optimizing DOT neuroimaging depth.
• Specific NN measurement strategies can be employed to target different brain tissue depths.
• Finite element modeling provides a robust method for assessing DOT sensitivity in complex head geometries.