A Novel Differential Pathlength Factor Model for Near-Infrared Diffuse Optical Imaging

TL;DR

This paper introduces new models for differential pathlength factors in near-infrared diffuse optical imaging, significantly reducing errors compared to traditional methods, especially at small source-detector distances.

Contribution

The authors develop two distance- and property-dependent DPF models using Monte Carlo simulations, improving accuracy over standard formulations in CW-NIR imaging.

Findings

Proposed models achieve errors below 10% across various optical conditions.

Conventional DPFs can have errors exceeding 100%.

Experimental validation confirms improved quantitative accuracy.

Abstract

Near infrared diffuse optical imaging can be performed in reflectance and transmission mode and relies on physical models along with measurements to extract information on changes in chromophore concentration. Continuous-wave near-infrared diffuse optical imaging relies on accurate differential pathlength factors (DPFs) for quantitative chromophore estimation. Existing DPF definitions inherit formulation-dependent limitations that can introduce large errors in modified Beer--Lambert law analyses. These errors are significantly higher at smaller source-detector separations in a reflectance mode of measurement. This minimizes their applicability in situations where large area detection is used and also when signal depth is varying. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models one ideal and one experimentally practical and benchmark them against standard formulations. The proposed models achieve errors below 10 percent across broad optical conditions, whereas conventional DPFs can exceed 100 percent error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.