Hsieh Y.-PKao T.-CKUNG-BIN SUNG2023-06-092023-06-0920210277786Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120479420&doi=10.1117%2f12.2615837&partnerID=40&md5=a6cbc46867008caecc133c56fb81909ehttps://scholars.lib.ntu.edu.tw/handle/123456789/632427Transcranial photobiomodulation (tPBM) has emerged as a novel non-invasive intervention for several neuropsychiatric or neurodegenerative conditions due to its neuroprotective and neuroenhancement effects by applying red/near-infrared (NIR) light to the forehead. tPBM has been applied to improve cognition in chronic traumatic brain injury, whereas tPBM-induced enhancement of the brain is dose-dependent and the effectiveness of each dose is affected by several factors such as the brain structure. In this study, we perform Monte Carlo simulations on 154 head models built with magnetic resonance images of healthy subjects, and propose a machine-learning based model that predicts the fraction of the energy of 1064-nm photons delivered to the gray matter (GM) based on the diffuse reflectance exiting the scalp surface (with wavelengths of 660, 730, 810, 850, and 940 nm) and demographic variables such as gender and age. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.Dosimetry; Monte Carlo methods; Photobiomodulation; Transcranial light stimulationBrain; Intelligent systems; Light; Magnetic resonance; Magnetic resonance imaging; Photons; Fluence rates; Invasive intervention; Light stimulation; MonteCarlo methods; Near infrared light; Photobiomodulation; Photon fluence; Prefrontal cortex; Transcranial; Transcranial light stimulation; Monte Carlo methodsconference paper10.1117/12.26158372-s2.0-85120479420