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    This study introduces U-N2C, an unsupervised framework for denoising Magnetic Particle Imaging System Matrices. It effectively preserves high-frequency details, improving image resolution by disentangling noise and content.

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    Area of Science:

    • Medical Imaging
    • Signal Processing

    Background:

    • Magnetic Particle Imaging (MPI) offers high resolution and sensitivity.
    • MPI reconstruction relies on a System Matrix, susceptible to noise during calibration.
    • Existing denoising methods degrade spatial resolution by removing high frequencies.

    Purpose of the Study:

    • To develop an unsupervised denoising framework for Magnetic Particle Imaging System Matrices.
    • To address the loss of high-frequency components and preserve spatial resolution.

    Main Methods:

    • Proposed U-N2C (Unsupervised-Noisy-Clean) framework utilizing dual memory blocks.
    • Pattern Memory Block with position-aware frequency index embedding for System Matrix patterns.
    • Noise Memory Block for implicit noise distribution approximation.
    • Generation of pseudo noisy-clean pairs for enhanced denoising.

    Main Results:

    • U-N2C achieves state-of-the-art performance in denoising System Matrices.
    • Demonstrated effectiveness on both synthetic and real noise datasets.
    • Preserves high-frequency components crucial for spatial resolution.

    Conclusions:

    • U-N2C offers a novel unsupervised approach for robust MPI System Matrix denoising.
    • The framework successfully disentangles noise and content, enhancing image quality.
    • Validated through extensive qualitative and quantitative ablation studies.