  Diffusion magnetic resonance imaging (MRI) is the only imaging modality for
non-invasive movement detection of in vivo water molecules, with significant
clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot
techniques can achieve higher resolution, better signal-to-noise ratio, and
lower geometric distortion than single-shot, but suffers from inter-shot
motion-induced artifacts. These artifacts cannot be removed prospectively,
leading to the absence of artifact-free training labels. Thus, the potential of
deep learning in multi-shot DWI reconstruction remains largely untapped. To
break the training data bottleneck, here, we propose a Physics-Informed Deep
DWI reconstruction method (PIDD) to synthesize high-quality paired training
data by leveraging the physical diffusion model (magnitude synthesis) and
inter-shot motion-induced phase model (motion phase synthesis). The network is
trained only once with 100,000 synthetic samples, achieving encouraging results
on multiple realistic in vivo data reconstructions. Advantages over
conventional methods include: (a) Better motion artifact suppression and
reconstruction stability; (b) Outstanding generalization to multi-scenario
reconstructions, including multi-resolution, multi-b-value,
multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical
adaptability to patients with verifications by seven experienced doctors
(p<0.001). In conclusion, PIDD presents a novel deep learning framework by
exploiting the power of MRI physics, providing a cost-effective and explainable
way to break the data bottleneck in deep learning medical imaging.
