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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Nanotechnology

    Background:

    • Magnetic particle imaging (MPI) is an emerging technique for visualizing magnetic nanoparticle distributions in biological tissues.
    • System-matrix (SM)-based reconstruction in MPI yields high image quality but requires time-consuming calibration measurements.
    • Recalibration is necessary due to changes in tracer characteristics or magnetic field, increasing labor and time demands.

    Purpose of the Study:

    • To develop a fast and efficient system-matrix (SM) calibration method for magnetic particle imaging (MPI).
    • To address the time-consuming nature of traditional SM calibration and the need for frequent recalibration.
    • To improve MPI image quality by optimizing SM calibration, particularly in scenarios with limited labeled data.

    Main Methods:

    • Leveraged transformer architecture with self-attention to encode inherent relationships between SM rows using coil channel and frequency index as multimodal information.
    • Proposed a pseudo-label-based progressive pretraining strategy to effectively utilize easily obtainable low-resolution SM data and mitigate overfitting.
    • Evaluated the method on public (OpenMPI) and simulation datasets, and on two in-house MPI scanners.

    Main Results:

    • The proposed transformer-based method significantly outperforms existing calibration techniques in speed and accuracy.
    • Achieved improved image resolution on in-house MPI scanners without the need for full-size SM measurements.
    • Ablation studies validated the effectiveness of modeling SM inter-row relations and the pretraining strategy.

    Conclusions:

    • The developed method offers a substantial advancement in accelerating MPI system-matrix calibration.
    • This approach enhances MPI's practical applicability by reducing calibration time and improving image reconstruction quality.
    • The findings pave the way for more efficient and accessible MPI systems in biomedical research and clinical settings.