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      • Department of Electrical and Electronics Engineering
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      Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery

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      Author(s)
      Yurt, Mahmut
      Özbey, Muzaffer
      Dar, Salman U.H.
      Tınaz, Berk
      Oğuz, Kader K.
      Çukur, Tolga
      Date
      2022-05
      Source Title
      Medical Image Analysis
      Print ISSN
      1361-8415
      Electronic ISSN
      1361-8423
      Publisher
      Elsevier BV
      Volume
      78
      Pages
      [1] - [19]
      Language
      English
      Type
      Article
      Item Usage Stats
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      4
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      Abstract
      Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multi- tude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to alleviate this limitation is to recover high-quality images from data undersampled across various dimensions, most commonly the Fourier domain or contrast sets. A primary distinction among recovery methods is whether the anatomy is processed per volume or per cross-section. Volumetric models offer enhanced capture of global contextual information, but they can suffer from suboptimal learning due to elevated model complexity. Cross-sectional models with lower complexity offer improved learning behavior, yet they ignore contextual information across the longitu- dinal dimension of the volume. Here, we introduce a novel progressive volumetrization strategy for gen- erative models (ProvoGAN) that serially decomposes complex volumetric image recovery tasks into suc- cessive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions. Provo-GAN effectively captures global context and recovers fine-structural details across all dimensions, while maintaining low model complexity and improved learning behavior. Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
      Keywords
      MRI
      Generative adversarial networks
      Synthesis
      Reconstruction
      Cross-section
      Model complexity
      Context
      Permalink
      http://hdl.handle.net/11693/111593
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.media.2022.102429
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