  Quantitative Susceptibility Mapping (QSM) is a technique for measuring
magnetic susceptibility of tissues, aiding in the detection of pathologies like
traumatic brain injury and multiple sclerosis by analyzing variations in
substances such as iron and calcium. Despite its clinical value, achieving
high-resolution QSM (voxel sizes < 1 mm3) reduces signal-to-noise ratio (SNR),
compromising diagnostic quality. To mitigate this, we applied the
Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising technique on
T2* weighted data, to enhance the quality of R2*, T2*, and QSM maps. Denoising
was tested on a numerical phantom, healthy subjects, and patients with brain
metastases and sickle cell disease, demonstrating effective and robust
improvements across different scan settings. Further analysis examined noise
propagation in R2* and T2* values, revealing lower noise-related variations in
R2* values compared to T2* values which tended to be overestimated due to
noise. Reduced variability was observed in QSM values post denoising,
demonstrating MP-PCA's potential to improve the
