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bioRxiv · 10.1101/2020.02.15.948976

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2 decay data

Wiggermann, V.·Vavasour, I. M.·Kolind, S.·MacKay, A. L.·Helms, G.·Rauscher, A.
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Abstract

Multi-compartment T2-mapping has gained particular relevance for the study of myelin water in brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non-negative least squares fitting of multi-echo T2 data has been widely employed for the computation of the myelin water fraction (MWF) and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B1+-homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi-compartment T2 and MWF maps. We generated multi-echo spin-echo T2 decay curves following the approach of CarrPurcell-Meiboom-Gill for various myelin concentrations and myelin T2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra-/extracellular water geometric mean (gm) T2. MWF estimates were more stable than myelin water gmT2 time across different settings of the T2 analysis. We observed that the lower limit of the T2 distribution grid should be slightly shorter than TE1. Both TE1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T2 signal. Among all parameters of interest, the estimated MWF and intra-/extracellular water gmT2 differed by approximately 0.13-4 percentage points and 3-4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B1+-inhomogeneities and at lower signal-to-noise ratio. Tailoring acquisition strategies may allow to better characterize the T2 distribution, including the myelin water, in vivo.

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Wiggermann, V., Vavasour, I. M., Kolind, S., MacKay, A. L., Helms, G., Rauscher, A.. 2020-02-17. Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2 decay data. https://doi.org/10.1101/2020.02.15.948976

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