Myelin water imaging data analysis in less than one minute Abstract   Purpose  Based on a deep learning neural network (NN) algorithm, a super fast and easy to implement data analysis method was proposed for myelin water imaging (MWI) to calculate the myelin water fraction (MWF).    Methods  A NN was constructed and trained on MWI data acquired by a 32echo 3D gradient and spin echo (GRASE) sequence. Ground truth labels were created by regularized nonnegative least squares (NNLS) with stimulated echo corrections. Voxelwise GRASE data from 4 healthy brains were used for NN training. The trained NN was tested on 2 healthy brains, 1 MS brain with segmented lesions, 1 healthy spinal cord, and 1 healthy brain acquired from a different scanner.    Results  Production of whole brain MWF maps in approximately 33xe2x80xafs can be achieved by a trained NN without graphics card acceleration. For all testing regions, no visual differences between NN and NNLS MWF maps were observed, and no obvious regional biases were found. Quantitatively, all voxels exhibited excellent agreement between NN and NNLS (all R2u003e0.98, pxe2x80xaf    Conclusion  The time for accurate MWF calculation can be dramatically reduced to less than 1xe2x80xafmin by the proposed NN, addressing one of the barriers facing future clinical feasibility of MWI.

A Multivariate Approach for Denoising of T2 Relaxation Decay Curves in Myelin Water Fraction Imaging Accurately measuring brain myelin content in vivo holds great promise for studying and understanding of number of brain diseases. A surrogate marker for myelin, myelin water fraction (MWF), can be measured with MR relaxation techniques that sample the T2 decay with multiple echoes. Low signaltonoise ratios in later echoes, suboptimal flip angles, and the fact that robust decomposition into multiple exponential curves is notoriously difficult, all conspire to reduce the accuracy of MWF estimates. The resulting maps are typically spatially noisy  despite the fact that adjacent white matter voxels are usually assumed to have similar myelin measures in vivo.Here we propose a spatiotemporal filtering process prior to the standard fitting based on a combination of multivariate empirical mode decomposition (MEMD) and multiset canonical correlation analysis (MCCA) to decompose and find the most robust temporal decay pattern among voxels that have similar overall decay curves and across the white matter. Based on enhanced spatial smoothness measures, increased testretest reliability within subjects, and decreased Coefficient of Variation of MWF scores, we suggest that the proposed approach provides enhanced accuracy of the ultimatelycomputed MWF maps.