Diffusion tensor imaging (DTI) requires several diffusion-weighted image (DWI) acquisitions, leading to a long scan time. In this study, a deep-learning model based on multi-slice information sharing was implemented to obtain ultrafast DTI. This method uses the similarity of DWIs among neighboring slices to minimize the requirement of the number of diffusion-encoding directions. The results indicate that the proposed method can reconstruct high-quality diffusion and DTI-derived maps, exceptionally robust to noise in fractional anisotropy mapping even when only 3-direction DWIs.
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