Abstract #4898

# Multi-exponential Relaxometry using $$\ell_1$$$-regularized Iterative NNLS (MERLIN) for Accurate and Robust Myelin Water Fraction Imaging Markus Zimmermann1, Ana-Maria Oros-Peusquens1, Zaheer Abbas1,2, Elene Iordanishvili1, Seonyeong Shin1, Seong Dae Yun1, and N. Jon Shah1,2,3,4 1Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany, 2Department of Neurology, RWTH Aachen University, Aachen, Germany, 3Institute of Neuroscience and Medicine 11, JARA, Forschungszentrum Jülich, Jülich, Germany, 4JARA - BRAIN, Translational Medicine, Aachen, Germany A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using MRI. Multi-exponential relaxometry is fundamentally ill-conditioned, and as such, is extremely sensitive to noise. MERLIN is a fully automated, multi-voxel approach that incorporates $$\ell_1$$$-regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is compared to the conventional $$\ell_2$$\$-regularized NNLS (rNNLS) in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3T. The estimated water fraction maps from MERLIN are spatially more consistent, more precise, and more accurate, reducing the root-mean-squared-error by up to 90 percent in simulations.

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