TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation
Year: 2026
Type: preprint
Abstract: Background Accurate modeling of photon transport through the heterogeneous tissues of the human head is important for individualized transcranial light stimulation (tLS). However, high-precision Monte Carlo (MC) simulations can be computationally demanding, and existing general-purpose simulation tools do not provide an integrated, brain-oriented workflow. Methods We developed TLS-Sim, a framework that integrates subject-specific head-model construction, multiple light-source configuration, GPU-accelerated MC photon simulation, visualization, and intracranial dosimetric analysis. Its central feature is a deep-learning-accelerated MC pathway, termed Flux-to-Flux, that estimates a high-photon-count energy-deposition field from a paired low-photon-count simulation. Sparse and full Monte Carlo fields were encoded using a three-dimensional variational autoencoder (VAE), and a three-dimensional velocity network was trained to transform the sparse latent representation toward the corresponding full-simulation representation. Results We demonstrate the end-to-end toolkit and the Flux-to-Flux (F2F) pathway on a representative individualized head model evaluated across three source configurations. Relative to the sparse simulation, the learned prediction improved agreement with the full Monte Carlo reference by approximately +4 dB in signal-to-noise ratio on average, with correspondingly higher structural similarity and lower mean absolute error. Conclusions TLS-Sim provides a modular, openly released computational framework for individualized tLS simulation and analysis. The Flux-to-Flux pathway is presented as an implemented capability that reduces the computational burden of high-photon-count simulation while improving spatial agreement with the full MC reference, most strongly at the low-fluence field boundaries where the sparse simulation is noisiest.
Institutions: Dalian University of Technology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Normal University, Chinese Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Peking University Third Hospital, Beijing Jiaotong University, Dalian University, University of Jyväskylä
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7212323462