Physics-informed Deep Diffusion MRI Reconstruction: Break the Data Bottleneck in Artificial Intelligence

Deep learning is widely employed in biomedical magnetic resonance image (MRI) reconstructions. However, accurate training data are unavailable in multi-shot interleaved echo planer imaging (Ms-iEPI) diffusion MRI (DWI) due to inter-shot motion. In this work, we propose a Physics-Informed Deep DWI reconstruction method (PIDD). For Ms-iEPI DWI data synthesis, a simplified physical motion model for motion-induced phase synthesis is proposed. Then, lots of synthetic phases are combined with a few real data to generate efficient training data. Extensive results show that, PIDD trained on synthetic data enables sub-second, ultra-fast, high-quality, and robust reconstruction with different b-values and undersampling patterns.