This work provides a new denoising methodology for multi-acquisition magnetic resonance images (MRI) based on principal components analysis (PCA). We are proposing a new principal component selection criterion that identifies spatial information in the extracted component coefficients, leading to a better preservation of anatomical structures and pathological information. In addition, our adaptive filtering step allows us to further denoise the MRI data, rejecting persistent spatial noise from the extracted component coefficients. In our investigations the proposed method outperformed the eigenvalue based selection criteria on Amide Proton Transfer weighted CEST data.
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