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Background and Purpose: A current algorithm to obtain a synthetic myelinvolume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI)has a potential problem, that it does not incorporate information fromsurrounding pixels. The purpose of this study was to develop a method thatutilizes a convolutional neural network (CNN) to overcome this problem.Methods: RSRI and magnetization transfer images from 20 healthy volunteers wereincluded. A CNN was trained to reconstruct RSRI-related metric maps into amyelin volume-related index (generated myelin volume index: GenMVI) map usingthe myelin volume index map calculated from magnetization transfer images(MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared bytesting how well they correlated with the MTMVI map. The correlations wereevaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and(ii) pixel-based comparison for ROIs defined in four different tissue types(cortical and subcortical gray matter, white matter, and whole brain). Results:For atlas-based ROIs, the overall correlation with the MTMVI map was higher forthe GenMVI map than for the SyMVF map. In the pixel-based comparison,correlation with the MTMVI map was stronger for the GenMVI map than for theSyMVF map, and the difference in the distribution for the volunteers wassignificant (Wilcoxon sign-rank test, P<.001) in all tissue types. Conclusion:The proposed method is useful, as it can incorporate more specific informationabout local tissue properties than the existing method.

The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging