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    Dual-Cycle Constrained Bijective VAE-GAN for Tagged-to-Cine Magnetic ResonanceE Image Synthesis

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    Author
    Liu, Xiaofeng
    Xing, Fangxu
    Prince, Jerry L
    Carass, Aaron
    Stone, Maureen
    El Fakhri, Georges
    Woo, Jonghye
    Date
    2021-05-25
    Journal
    Proceedings. IEEE International Symposium on Biomedical Imaging
    Publisher
    IEEE
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1109/isbi48211.2021.9433852
    http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8547333/
    Abstract
    Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.
    Keyword
    Generative adversarial networks
    Tagged MRI
    deep learning
    image synthesis
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/17043
    ae974a485f413a2113503eed53cd6c53
    10.1109/isbi48211.2021.9433852
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