Resolving layer-specific optical properties with time-shift inversion to mitigate crosstalk in continuous-wave fNIRS optical topography

Functional near-infrared spectroscopy (fNIRS) enables portable, non-invasive monitoring of cerebral oxygenation, yet its quantitative accuracy in continuous-wave optical topography (CW-fNIRS-OT) is often constrained by conventional methods that rely on an empirical differential path length factor (DPF). The empirical DPF typically assumes homogeneous tissue and ignores the layered head structure as well as inter-subject anatomical variability. In this study, we overcome the DPF-induced quantitative limitations in CW-fNIRS-OT by introducing a time-shift inversion strategy that, in tandem with layer-sensitive time-gated windows, achieves an efficient optical properties inversion while effectively decoupling and compensating for path length-dependent distortions. Leveraging a statistical brain model derived from anatomical atlases, our approach synthesizes subject-specific head geometry from simple anthropometric data, explicitly eliminating MRI dependency. Photon-transport simulations are employed to predefine time gates with preferential sensitivity to superficial and deep layers, while a dynamic time-shift correction compensates for residual inter-subject geometric mismatches. Ultimately, this design permits the precise inversion of layer-specific optical properties via a computationally lightweight single-layer diffusion equation model. Validation through numerical simulations, phantom experiments, and in vivo breath-holding tasks demonstrates that the proposed method significantly outperforms conventional empirical-DPF approaches. It corrects systematic path length errors, achieving a quantitativeness ratio of ∼0.95 vs ∼0.85 for conventional methods, and improves spatial fidelity, contrast-to-noise ratio, and task-state discriminability in SVM classification. Affording personalized, high-accuracy DPF inversion with computational efficiency, this framework provides a clinically viable route to individualized cerebral oxygenation assessment and is poised to catalyze the broader adoption of fNIRS in both neuroscientific inquiry and diagnostic practice.