AI model enables more than a million-fold acceleration of diffuse optical tomography for real-time diagnosis

Researchers at University of Tsukuba have developed an AI model capable of predicting light propagation in biological tissue in diffuse optical tomography, a noninvasive imaging technique for detecting abnormalities such as hemorrhages and tumors. The model performs these calculations in approximately 2 milliseconds, exceeding the speed of conventional simulation methods by more than 1 million times, paving the way for real-time diagnostic applications. The paper is published in Biomedical Engineering Letters.

Diffuse optical tomography detects internal abnormalities by illuminating biological tissue with near-infrared light, without causing radiation exposure or damage. However, high diagnostic accuracy depends on solving the radiative transfer equation that models light propagation within tissue. Since these numerical simulations can take several hours per calculation, using this method for real-time diagnosis remains challenging.

To eliminate computationally intensive simulations, this study introduces a neural network-based machine learning model that serves as an ultra-fast emulator. Trained on extensive simulation data, the model predicts time-resolved light signals detected at measurement points based on the location and size of an abnormal region.

The model demonstrates robust generalization, accurately reproducing signals, even for unseen parameter combinations, with accuracy limited only by the noise level in the training data. Each inference takes approximately 2 milliseconds, representing a speedup of more than 1 million times compared with conventional simulation methods. This dramatic acceleration enables efficient exploration of the vast parameter spaces required for diagnostic analysis.

Furthermore, by combining the AI model with statistical sampling techniques, the model enabled the researchers to accurately estimate the location and size of abnormal regions from optical signals. These findings highlight this model as a promising foundational tool for real-time diagnosis of cerebral hemorrhage and tumors.

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Lisa Lock

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Robert Egan

Bachelor’s in mathematical biology, Master’s in creative writing. Well-traveled with unique perspectives on science and language.

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