Authors
Shu Horie, Hidenobu Yajima, Makito Abe, Masayuki Umemura
Published in
Biomedical engineering letters. Volume 16. Issue 5. Pages 1243-1259. Epub Apr 18, 2026.
Abstract
Time-domain diffuse optical tomography (TD-DOT) is a powerful method for diagnosing anomalies in biological tissue such as brain hemorrhage and tumor. However, numerical simulations for TD-DOT require exploring a large number of parameter combinations and demand substantial computational resources. To address this challenge, we develop a neural network (NN) that can rapidly infer time-resolved signals from given tissue parameters. A high-quality training dataset for the NN is generated using ray-tracing-based radiative transfer simulations for 640 different absorber parameter combinations. Using the simulation data, we utilize NN to construct an emulator reproducing time-resolved signals for any parameters not used in the training data. We train two NN models with different training datasets: one with Gaussian noise added and the other without Gaussian noise. The NN trained with noisy data demonstrates superior performance, accurately reproducing time-resolved signals for unseen parameters. Its errors remain comparable to the noise level in the training data, highlighting strong robustness and generalization capability. Each inference takes only [Formula: see text] seconds, which is [Formula: see text] times faster than a direct radiative transfer simulation. This drastic speedup suggests the potential for efficient inverse problem analysis and application in real-time clinical diagnosis.
PMID:
42801013
Bibliographic data and abstract were imported from PubMed on 27 Sep 2026.
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