A fully connected neural network (FCN) was trained to map a short diffusion weighted image acquisition to high quality Quasi-Diffusion Imaging (QDI) parameter maps. The FCN produced denoised and enhanced QDI parameter maps compared to weighted least squares fitting of data to the QDI model. The FCN shows generalisation to unseen pathology such as grade IV glioma dMRI data and demonstrates the FCN can produce high quality QDI tensor maps from clinically feasible 2 minute data acquisitions. An FCN further enhances the ability of QDI to provide non-Gaussian diffusion imaging within clinically feasible acquisition times.
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