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      • Dept. of Electrical and Electronics Engineering - Master's degree
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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      Deep learning for accelerated 3D MRI

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      Author(s)
      Özbey, Muzaffer
      Advisor
      Çukur, Tolga
      Date
      2021-08
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      256
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      Abstract
      Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude 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 sub-optimal learning due to elevated model complexity. Cross-sectional models with lower complexity offer improved learning behavior, yet they ignore contextual information across the longitudinal dimension of the volume. Here, we introduce a novel progressive volumetrization strategy for generative models (ProvoGAN) that serially decomposes complex volumetric image recovery tasks into succes-sive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions. ProvoGAN effectively captures global context and recovers fine-structural details across all dimensions, while maintaining low model complexity and improved learning behaviour. Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior per-formance to state-of-the-art volumetric and cross-sectional models.
      Keywords
      MRI
      Generative adversarial networks
      Synthesis
      Reconstruction
      Model complexity
      Permalink
      http://hdl.handle.net/11693/76506
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      • Dept. of Electrical and Electronics Engineering - Master's degree 655
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