DWI can probe tissue microstructures in many disease processes over a broad range of b-values. In the scenario where severe geometric distortion presents, non-single-shot EPI techniques can be used, but introduce other issues such as lengthened acquisition times, which often requires undersampling in kspace. Deep learning has been demonstrated to achieve many-fold undersampling especially when highly redundant information is present. In this study, we have applied a novel convolutional recurrent neural network (CRNN) to reconstruct highly undersampled (up to six-fold) multi-b-value, multi-direction DWI dataset by exploiting the information redundancy in the multiple b-values and diffusion gradient directions.
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