Rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled magnetic resonance imaging data using deep neural network

Changes in myelination are a cardinal feature of brain development and the pathophysiology of several cerebral diseases, including multiple sclerosis and dementias. Advanced magnetic resonance imaging (MRI) methods have been developed to probe myelin content through the measurement of myelin water fraction (MWF). However, the prolonged data acquisition and post-processing times of current MWF mapping methods pose substantial hurdles to their clinical implementation. Recently, fast steady-state MRI sequences have been implemented to produce high spatial resolution whole-brain MWF mapping within [~] 20 min. Despite the subsequent significant advances in the inversion algorithm to derive MWF maps from steady-state MRI, the high-dimensional nature of such inversion does not permit further reduction of the acquisition time by data under-sampling. In this work, we present an unprecedented reduction in the computation ([~] 30 s) and the acquisition time ([~] 7 min) required for whole-brain high-resolution MWF mapping through a new Neural Network (NN)-based approach, named: Relaxometry of Extremely Under-SamplEd Data (NN-REUSED). Our analyses demonstrate virtually similar accuracy and precision in derived MWF values using the NN-REUSED approach as compared to results derived from the fully-sampled reference method. The reduction in the acquisition and computation times represents a breakthrough toward clinically practical MWF mapping.