Keywords: Multi-Contrast, Multi-Contrast, MR histology, multi-parametric MRI, deep learning, diffusion, magnetization transfer, relaxivity, mouse brain
Motivation: Deep neural networks trained with MRI and myelin histology data offer enhanced sensitivity and specificity compared to conventional MRI markers, yet their inner workings remain unknown.
Goal(s): To elucidate the relationships between MRI and myelin histology.
Approach: We mapped multi-parametric MRI data of developing mouse brains and their myelin content onto a 3D manifold after dimension reduction and defined the relationships between MRI and myelin signals in a piecewise fashion.
Results: Our findings revealed how the relationships between multiple MRI parameters and tissue myelin content evolved throughout brain development.
Impact: We have developed a novel data-driven approach to characterize the complex relationship between MRI parameters and myelin. The results suggest that multi-parametric MRI is necessary for accurate myelin mapping.
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