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    This study introduces a novel compressed sensing method for low field magnetic resonance imaging. It effectively corrects magnetic field inhomogeneity, reducing image blurring and artifacts without extra scans.

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    Area of Science:

    • Magnetic Resonance Imaging
    • Image Reconstruction
    • Biomedical Engineering

    Background:

    • Low field magnetic resonance imaging (LF-MRI) systems suffer from significant magnetic field inhomogeneity.
    • This inhomogeneity leads to image blurring and artifacts, limiting diagnostic quality.
    • Existing off-resonance correction methods often require additional radio-frequency pulses or acquisitions, increasing scan time.

    Purpose of the Study:

    • To develop an efficient compressed sensing method for LF-MRI.
    • To implement a self-calibrated off-resonance correction technique.
    • To improve the quality of LF-MRI by reducing blurring and artifacts.

    Main Methods:

    • A compressed sensing framework utilizing cross-sampling was employed.
    • Off-resonance correction was achieved through self-calibration based on image registration.
    • Magnetic field inhomogeneity was estimated using first-order components for fast computation.

    Main Results:

    • The proposed method successfully corrected off-resonance effects without additional RF pulses or acquisitions.
    • Image registration-based estimation of magnetic field inhomogeneity proved effective.
    • Experiments on phantoms and a chemically fixed mouse demonstrated significant reduction in blurring and artifacts.

    Conclusions:

    • The developed compressed sensing method with self-calibrated off-resonance correction is a practical and efficient solution for LF-MRI.
    • This technique enhances image quality in low field settings, making LF-MRI more viable.
    • The method offers advantages in speed and simplicity compared to conventional correction techniques.