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  • On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks

    Author(s)
    Liu, Xinwen
    Wang, Jing
    Sun, Hongfu
    Chandra, Shekhar S
    Crozier, Stuart
    Liu, Feng
    Griffith University Author(s)
    Wang, Jing
    Year published
    2021
    Metadata
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    Abstract
    Multi-contrast (MC) Magnetic Resonance Imaging (MRI) of the same patient usually requires long scanning times, despite the images sharing redundant information. In this work, we propose a new iterative network that utilizes the sharable information among MC images for MRI acceleration. The proposed network has reinforced data fidelity control and anatomy guidance through an iterative optimization procedure of Gradient Descent, leading to reduced uncertainties and improved reconstruction results. Through a convolutional network, the new method incorporates a learnable regularization unit that is capable of extracting, fusing, ...
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    Multi-contrast (MC) Magnetic Resonance Imaging (MRI) of the same patient usually requires long scanning times, despite the images sharing redundant information. In this work, we propose a new iterative network that utilizes the sharable information among MC images for MRI acceleration. The proposed network has reinforced data fidelity control and anatomy guidance through an iterative optimization procedure of Gradient Descent, leading to reduced uncertainties and improved reconstruction results. Through a convolutional network, the new method incorporates a learnable regularization unit that is capable of extracting, fusing, and mapping shareable information among different contrasts. Specifically, a dilated inception block is proposed to promote multi-scale feature extractions and increase the receptive field diversity for contextual information incorporation. Lastly, an optimal MC information feeding protocol is built through the design of a complementary feature extractor block. Comprehensive experiments demonstrated the superiority of the proposed network, both qualitatively and quantitatively.
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    Journal Title
    Magnetic Resonance Imaging
    Volume
    77
    DOI
    https://doi.org/10.1016/j.mri.2020.12.019
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Biomedical Engineering
    Clinical Sciences
    Cognitive Sciences
    Deep learning
    Image reconstruction
    Magnetic resonance imaging (MRI)
    Multi-contrast
    Publication URI
    http://hdl.handle.net/10072/401058
    Collection
    • Journal articles

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