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Abstract #0360

A Bayesian Algorithm Using Spatial Priors for Multi-Exponential T2 Relaxometry from Multi-Echo Spin Echo MRI

MAGNA25Dushyant Kumar1, Thanh Nguyen2, Susan Gauthier3, Ashish Raj2

1Neuroradiology, University of Hamburg, Hamburg, Germany; 2Radiology, Weill Cornell Medical College, Newyork, NY, United States; 3neurology, Weill Cornell Medical College, Newyork, NY, United States

Problem: Because of the ill-posedness of the inverse problem with more unknowns than number of echoes, the accurate quantification of myelin water fraction (MWF) from T2-relaxometry requires high SNR (~500-1000). Methods: The voxelwise conventional regularization is performed followed by implementation of the spatial smoothness over local neighborhood using proposed spatial approach. Results: The inferred MWF-map has significantly reduced spatial variations resulting in better tissue-differentiation and is superior to conventionally regularized version based on various criteria. Conclusions: Spatial constraints allow the handling of lower SNR data which may allow better MWF reproducibility for longitudinal or multi-site studies and warrants further evaluation.