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Noise Propagation and MP-PCA Image Denoising for High-Resolution Quantitative $R_2^{\rm{*}}$, $T_2^{\rm{*}}$, and

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    Quantitative Susceptibility Mapping (QSM) can now achieve high resolution without sacrificing signal quality. This breakthrough enhances diagnostic capabilities for neurological diseases by improving imaging of iron and calcium variations.

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    Area of Science:

    • Neuroimaging
    • Medical Physics

    Background:

    • Quantitative Susceptibility Mapping (QSM) is crucial for detecting neurological pathologies by analyzing tissue magnetic susceptibility.
    • High-resolution QSM (voxel sizes < 1 mm3) is vital for detailed diagnostics but suffers from reduced signal-to-noise ratio (SNR).

    Purpose of the Study:

    • To introduce and validate a denoising technique for enhancing the quality of QSM data.
    • To enable high-resolution QSM acquisition within clinical time constraints without compromising diagnostic quality.

    Main Methods:

    • Marchenko-Pastur Principal Component Analysis (MP-PCA) was employed for denoising T2*-weighted data.
    • The denoising technique was validated using numerical phantoms, healthy subjects, and patients with brain metastases and sickle cell anemia.

    Main Results:

    • MP-PCA effectively denoised T2*-weighted data, improving SNR and accuracy across various scan settings.
    • Noise propagation analysis revealed noise augmentation in T2*-weighted values compared to R2* values.

    Conclusions:

    • MP-PCA denoising enables high-resolution (∼0.5 mm3) QSM acquisition at clinical scan times while maintaining adequate SNR.
    • This enhanced QSM pipeline can improve the diagnosis of neurological diseases through improved mapping of small vessels and mineral variations.