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dc.contributor.authorGuo, Zhen
dc.contributor.authorSong, Jung Ki
dc.contributor.authorBarbastathis, George
dc.contributor.authorGlinsky, Michael E
dc.contributor.authorVaughan, Courtenay T
dc.contributor.authorLarson, Kurt W
dc.contributor.authorAlpert, Bradley K
dc.contributor.authorLevine, Zachary H
dc.date.accessioned2023-05-19T13:52:37Z
dc.date.available2023-05-19T13:52:37Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/150780
dc.description.abstract<jats:p>X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known <jats:italic>a priori</jats:italic>, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.</jats:p>en_US
dc.language.isoen
dc.publisherOptica Publishing Groupen_US
dc.relation.isversionof10.1364/OE.460208en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceOpticaen_US
dc.titlePhysics-assisted generative adversarial network for X-ray tomographyen_US
dc.typeArticleen_US
dc.identifier.citationGuo, Zhen, Song, Jung Ki, Barbastathis, George, Glinsky, Michael E, Vaughan, Courtenay T et al. 2022. "Physics-assisted generative adversarial network for X-ray tomography." Optics Express, 30 (13).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.relation.journalOptics Expressen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-05-19T13:50:19Z
dspace.orderedauthorsGuo, Z; Song, JK; Barbastathis, G; Glinsky, ME; Vaughan, CT; Larson, KW; Alpert, BK; Levine, ZHen_US
dspace.date.submission2023-05-19T13:50:21Z
mit.journal.volume30en_US
mit.journal.issue13en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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