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Abstract #2757

Evaluation of Marchenko-Pastur PCA denoising on Multi-Exponential Relaxometry

Mark D. Does1, Jonas Lynge Olesen2, Kevin D Harkins3, Teresa Serradas-Duarte4, Sune N Jespersen2, and Noam Shemesh4

1Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 2Aarhus University, Aarhus, Denmark, 3Vanderbilt University, Nashville, TN, United States, 4Champalimaud Centre for the Unknown, Lisbon, Portugal

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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