• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Unsupervised MRI reconstruction via zero-shot learned adversarial transformers

      Thumbnail
      View / Download
      16.7 Mb
      Author(s)
      Korkmaz, Yilmaz
      Dar, Salman U.H.
      Yurt, Mahmut
      Özbey, Muzaffer
      Çukur, Tolga
      Date
      2022-01-27
      Source Title
      IEEE Transactions on Medical Imaging
      Print ISSN
      0278-0062
      Electronic ISSN
      1558-254X
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      41
      Issue
      7
      Pages
      1747 - 1763
      Language
      English
      Type
      Article
      Item Usage Stats
      8
      views
      11
      downloads
      Abstract
      Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods.
      Keywords
      Adversarial
      Transformers
      MRI
      Unsupervised
      Reconstruction
      Zero shot
      Generative
      Permalink
      http://hdl.handle.net/11693/111353
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TMI.2022.3147426
      Collections
      • Department of Electrical and Electronics Engineering 4011
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy