• Alireza Akhondi-Asl, Onur Afacan, Mukund Balasubramanian, Robert V Mulkern, and Simon K Warfield.
• Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA. Alireza.Akhondi-asl@childrens.harvard.edu.
• Magn Reson Med. 2016 Oct 1; 76 (4): 1301-13.

PurposeT2 relaxometry based on multiexponential fitting to a single slice multiecho sequence has been the most common MRI technique for myelin water fraction mapping, where the short T2 is associated with myelin water. However, very long acquisition times and physically unrealistic models for T2 distribution are limitations of this approach. We present a novel framework for myelin imaging which substantially increases the imaging speed and myelin water fraction estimation accuracy.MethodWe used the 2D multislice Carr-Purcell-Meiboom-Gill sequence to increase the volume coverage. To compensate for nonideal slice profiles, we numerically solved the Bloch equations for a range of T2 and B1 inhomogeneity scales to construct the bases for the estimation of the T2 distribution. We used a finite mixture of continuous parametric distributions to describe the complete T2 spectrum and used the constrained variable projection optimization algorithm to estimate myelin water fraction. To validate our model, synthetic, phantom, and in vivo brain experiments were conducted.ResultsUsing the Bloch equations, we can model the slice profile and construct the forward model of the T2 curve. Our method estimated myelin water fraction with smaller error than the nonnegative least squares algorithm.ConclusionsThe proposed framework can be used for reliable whole brain myelin imaging with a resolution of 2×2×4  mm3 in ≈17  min. Magn Reson Med 76:1301-1313, 2016. © 2015 Wiley Periodicals, Inc.© 2015 Wiley Periodicals, Inc.

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