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      • Department of Electrical and Electronics Engineering
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      Semi-supervised learning of MRI synthesis without fully-sampled ground truths

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      Author(s)
      Yurt, Mahmut
      Dalmaz, Onat
      Dar, Salman
      Özbey, Muzaffer
      Tınaz, Berk
      Oğuz, Kader
      Çukur, Tolga
      Date
      2022-08-16
      Source Title
      IEEE Transactions on Medical Imaging
      Print ISSN
      0278-0062
      Electronic ISSN
      1558-254X
      Publisher
      IEEE
      Volume
      41
      Issue
      12
      Pages
      3895 - 3906
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.
      Keywords
      Magnetic resonance imaging
      Image synthesis
      Semi-supervised
      Adversarial
      Undersampled
      Permalink
      http://hdl.handle.net/11693/111406
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TMI.2022.3199155
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