MRI relaxometry is a powerful tool for characterizing tissue at the sub-voxel level, such as for myelin water imaging. However, a major impediment to its use is the high signal-to-noise ratio requirement. Here, we propose Marchenko-Pastur principal component analysis—previously proposed for diffusion MRI—to denoise relaxometry data. Experimental studies and simulations exemplify the utility of this denoising, and its potential to accelerate data acquisition by 6-8X or more without bias in fitted relaxometry measures or degradation of image resolution. This simple yet important denoising step thus paves the way for broader applicability of relaxometry.
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