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    This study introduces a modified U-net to reduce undersampling artifacts in cardiac MRI. The novel deep learning approach achieves superior image quality and faster training, even with limited data.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiovascular Imaging

    Background:

    • Undersampling artifacts degrade image quality in 2D golden-angle radial cine cardiac MRI.
    • Deep learning methods offer potential for artifact reduction but require efficient training and robust performance.

    Purpose of the Study:

    • To develop and evaluate a modified U-net for reducing undersampling artifacts in cardiac MRI.
    • To compare the proposed method against existing deep learning and iterative reconstruction techniques.

    Main Methods:

    • A modified U-net architecture was trained on 2D spatio-temporal slices from cardiac MRI sequences.
    • The method was benchmarked against 2D/3D deep learning, compressed sensing, and iterative reconstruction approaches.

    Main Results:

    • The modified U-net significantly improved image quality compared to previous methods.
    • It achieved comparable results to 3D spatio-temporal U-net with reduced training time and data requirements.
    • The method demonstrated robustness to image rotation and suitability for limited training datasets.

    Conclusions:

    • The proposed spatio-temporal U-net effectively reduces undersampling artifacts in cardiac MRI.
    • Its efficiency in training and data requirements makes it ideal for clinical applications with limited data.
    • The method offers a robust and competitive alternative to existing reconstruction techniques.