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Unrolled-DOT: an interpretable deep network for diffuse optical tomography
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DOI：10.1117/1.JBO.28.3.036002.png)
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Significance: Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning. Aim: We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch. Approach: Our model Unrolled-DOT uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers. Results: In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10x reduction in runtime and mean-squared error, compared to traditional physics-based solvers. Conclusion: We demonstrated a learning-based ToF-DOT inverse solver that achieves state-ofthe-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Keyword:
optical tomography
machine learning
time-of-flight imaging
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