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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Motion artifacts degrade Magnetic Resonance Imaging (MRI) quality, especially at high acceleration factors.
    • Accurate motion estimation is crucial for rapid, high-quality motion-compensated MRI reconstruction.
    • Current methods struggle with artifacts caused by undersampling in accelerated MRI.

    Purpose of the Study:

    • To introduce an attention-aware deep learning framework for non-rigid pairwise registration in fully sampled and accelerated MRI.
    • To enable accurate motion estimation at high acceleration factors for improved MRI reconstruction.
    • To evaluate the framework's performance across different sampling trajectories and motion types.

    Main Methods:

    • Developed an attention-aware deep learning framework utilizing local visual representations and a transformer-based module.
    • Extracted similarity maps at multiple resolutions, incorporating long-range contextual information.
    • Combined local and global dependencies for simultaneous coarse and fine motion estimation.

    Main Results:

    • The framework achieved reliable and consistent motion fields for cardiac (up to 16x) and respiratory (up to 30x) motion.
    • Demonstrated superior qualitative and quantitative image quality in motion-compensated reconstruction compared to existing methods.
    • Validated on diverse datasets including fully sampled and accelerated cardiac and thoracic MRI from 101 patients and 62 healthy subjects.

    Conclusions:

    • The proposed attention-aware deep learning framework enables accurate motion estimation in accelerated MRI.
    • It significantly enhances the quality of motion-compensated MRI reconstruction, overcoming challenges posed by undersampling artifacts.
    • The method shows robustness across various acceleration factors and sampling trajectories.