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      • Department of Electrical and Electronics Engineering
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      Multi-contrast MRI synthesis with channel-exchanging-network

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      Author(s)
      Dalmaz, Onat
      Aytekin, İdil
      Dar, Salman Ul Hassan
      Erdem, Aykut
      Erdem, Erkut
      Çukur, Tolga
      Date
      2022-08-29
      Source Title
      Signal Processing and Communications Applications Conference (SIU)
      Print ISSN
      2165-0608
      Publisher
      IEEE
      Pages
      [1] - [4]
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Magnetic resonance imaging (MRI) is used in many diagnostic applications as it has a high soft-tissue contrast and is a non-invasive medical imaging method. MR signal levels differs according to the parameters T1, T2 and PD that change with respect to the chemical structure of the tissues. However, long scan times might limit acquiring images from multiple contrasts or if the multi-contrasts images are acquired, the contrasts are noisy. To overcome this limitation of MRI, multi-contrast synthesis can be utilized. In this paper, we propose a deep learning method based on Channel-Exchanging-Network (CEN) for multi-contrast image synthesis. Demonstrations are provided on IXI dataset. The proposed model based on CEN is compared against alternative methods based on CNNs and GANs. Our results show that the proposed model achieves superior performance to the competing methods.
      Keywords
      Multimodal fusion
      Channel-exchanging-network
      Multi-contrast image synthesis
      Deep learning
      Permalink
      http://hdl.handle.net/11693/111325
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/SIU55565.2022.9864937
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      • Department of Electrical and Electronics Engineering 4011
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