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<p> 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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sim 2\times 10^{-3}\)</EquationSource> </InlineEquation> seconds, which is <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(&gt;10^6\)</EquationSource> </InlineEquation> 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.</p>

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Development of a neural network predicting signals for time-domain diffuse optical tomography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Shu Horie,
  • Hidenobu Yajima,
  • Makito Abe,
  • Masayuki Umemura

摘要

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 \(\sim 2\times 10^{-3}\) seconds, which is \(>10^6\) 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.