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Diffusion magnetic resonance imaging (MRI) is the only imaging modality fornon-invasive movement detection of in vivo water molecules, with significantclinical and research applications. Diffusion MRI (DWI) acquired by multi-shottechniques can achieve higher resolution, better signal-to-noise ratio, andlower geometric distortion than single-shot, but suffers from inter-shotmotion-induced artifacts. These artifacts cannot be removed prospectively,leading to the absence of artifact-free training labels. Thus, the potential ofdeep learning in multi-shot DWI reconstruction remains largely untapped. Tobreak the training data bottleneck, here, we propose a Physics-Informed DeepDWI reconstruction method (PIDD) to synthesize high-quality paired trainingdata by leveraging the physical diffusion model (magnitude synthesis) andinter-shot motion-induced phase model (motion phase synthesis). The network istrained only once with 100,000 synthetic samples, achieving encouraging resultson multiple realistic in vivo data reconstructions. Advantages overconventional methods include: (a) Better motion artifact suppression andreconstruction stability; (b) Outstanding generalization to multi-scenarioreconstructions, including multi-resolution, multi-b-value,multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinicaladaptability to patients with verifications by seven experienced doctors(p<0.001). In conclusion, PIDD presents a novel deep learning framework byexploiting the power of MRI physics, providing a cost-effective and explainableway to break the data bottleneck in deep learning medical imaging.

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence