Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Magnetic Resonance Fingerprinting
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.
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