Motivation: Deep learning (DL)-assisted magnetic resonance fingerprinting (MRF) provides the opportunity for accurate parameter mapping with highly accelerated data acquisition. Goal(s): To develop a DL-assisted approach for high-resolution T1 and T2 mapping of the entire rodent brain using 3D MRF. Approach: A U-Net-based network was developed and trained on synthetic data generated from a mouse brain atlas. Its performance was evaluated with both phantom and in vivo experiments. Results: The network demonstrated robust, accurate T1 and T2 mapping at high undersampling rates, suggesting that whole-brain MRF can be achieved at reduced acquisition time without compromising accuracy. Impact: We present a novel deep learning-based 3D MRF method for accurate T1 and T2 mapping of the entire rodent brain using highly undersampled data, enabling dynamic MRF acquisition at higher temporal resolution.
Chen et al. (Tue,) studied this question.