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    POCS Augmented CycleGAN for MR Image Reconstruction

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    Genre
    Thesis/Dissertation
    Date
    2020
    Author
    Yang, Hanlu
    Advisor
    Bai, Li
    Committee member
    Bai, Li
    Wang, Ze
    Ahmad, Fauzia (Electrical engineer)
    Department
    Electrical and Computer Engineering
    Subject
    Electrical Engineering
    Compressed Sensing
    Cyclegan
    Deep Learning
    Mr Image Reconstruction
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/4073
    
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    DOI
    http://dx.doi.org/10.34944/dspace/4055
    Abstract
    Traditional Magnetic Resonance Imaging (MRI) reconstruction methods, which may be highly time-consuming and sensitive to noise, heavily depend on solving nonlinear optimization problems. By contrast, deep learning (DL)-based reconstruction methods do not need any explicit analytical data model and are robust to noise due to its large data-based training, which both make DL a versatile tool for fast and high-fidelity MR image reconstruction. While DL can be performed completely independently of traditional methods, it can, in fact, benefit from incorporating these established methods to achieve better results. To test this hypothesis, we proposed a hybrid DL-based MR image reconstruction method, which combines two state-of-the-art deep learning networks, U-Net and Generative Adversarial Network with Cycle loss (CycleGAN), with a traditional data reconstruction method: Projection Onto Convex Sets (POCS). Experiments were then performed to evaluate the method by comparing it to several existing state-of-the-art methods. Our results demonstrate that the proposed method outperformed the current state-of-the-art in terms of higher peak signal-to-noise ratio (PSNR) and higher Structural Similarity Index (SSIM).
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