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Abstract #3178

Denoising dynamic CrCEST in skeletal muscle following exercise using low rank tensor approximations

Neil Wilson1, Mark A Elliott2, Dushyant Kumar2, and Ravinder Reddy2
1Siemens Medical Solutions USA Inc, Malvern, PA, United States, 2CMROI, University of Pennsylvania, Philadelphia, PA, United States

Dynamic CrCEST allows for high resolution mapping of the creatine kinase reaction recovery kinetics following exercise. However, voxel averaging over large ROIs is usually done to get reliable fits because of high variation due to limited SNR. Here, we show that improved denoising utilizing low rank tensor approximations that exploit the full dimensionality of the scans allows for reliable fits over smaller volumes or even single voxels, making muscle response heterogeneity measurable.

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