Unrolled-DOT: an interpretable deep network for diffuse optical tomography

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dc.citation.articleNumber036002en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleJournal of Biomedical Opticsen_US
dc.citation.volumeNumber28en_US
dc.contributor.authorZhao, Yongyien_US
dc.contributor.authorRaghuram, Ankiten_US
dc.contributor.authorWang, Fayen_US
dc.contributor.authorKim, Stephen Hyunkeolen_US
dc.contributor.authorHielscher, Andreas H.en_US
dc.contributor.authorRobinson, Jacob T.en_US
dc.contributor.authorVeeraraghavan, Ashoken_US
dc.date.accessioned2023-04-25T14:47:35Zen_US
dc.date.available2023-04-25T14:47:35Zen_US
dc.date.issued2023en_US
dc.description.abstractSignificanceImaging 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.AimWe 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.ApproachOur 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.ResultsIn experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers.ConclusionWe demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.en_US
dc.identifier.citationZhao, Yongyi, Raghuram, Ankit, Wang, Fay, et al.. "Unrolled-DOT: an interpretable deep network for diffuse optical tomography." <i>Journal of Biomedical Optics,</i> 28, no. 3 (2023) SPIE: https://doi.org/10.1117/1.JBO.28.3.036002.en_US
dc.identifier.digital036002_1en_US
dc.identifier.doihttps://doi.org/10.1117/1.JBO.28.3.036002en_US
dc.identifier.urihttps://hdl.handle.net/1911/114806en_US
dc.language.isoengen_US
dc.publisherSPIEen_US
dc.rightsPublished 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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleUnrolled-DOT: an interpretable deep network for diffuse optical tomographyen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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