Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framewo...
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to corre...
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Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
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TN_cdi_proquest_journals_2392840702
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2392840702
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2331-8422
