Differential pathlength factor model for near-infrared diffuse optical imaging

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. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models—one ideal and the other experimentally practicalhich we label as our new Inversedistance Model here). We benchmark them against existing formulations. The proposed models achieve errors below 15% across broad optical conditions, whereas conventional DPFs can exceed 100% error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.