Quantitative simulation of near-infrared light treatment for Alzheimer's disease using patient-individualized optical-parametric phantoms.

Significance: Alzheimer's disease (AD) is a brain disorder characterized by its multifactorial nature and complex pathogenesis, highlighting the necessity for multimodal and individualized interventions. Among emerging therapies, near-infrared (NIR) light treatment shows promise as a therapeutic modality for AD. However, existing clinical studies lack sufficient data on light dosimetry, parameter optimization, and dose-response.

Aim: A versatile framework was developed to enable patient-individualized Monte Carlo simulation. A standardized dataset was established, including digital phantoms derived from 20 AD patients who received NIR light treatment.

Approach: The phantoms were synthesized and mapped with multispectral optical parameters, integrating cortical parcellation, subcortical segmentation, and sparse annotation. Structure-related light fluence pathways and dose-response relationships were elucidated using simulation results and cognitive/functional assessments.

Results: The capability for enhancing simulation fidelity and exploring dose-response relationships was verified using standard templates and clinical data. Linear independence was identified between changes in activities of daily living scale scores and energy deposition in gray matter.

Conclusions: The framework offers a solution toward dose-response analysis, parameter optimization, and safety control in the clinical translation for multiple treatment paradigms, demonstrating promise for individualized, standardized, and precise intervention planning.