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Abstract #2608

Image enhancement of Quasi-Diffusion Imaging using a fully connected neural network

Ian Robert Storey1, Catherine Anne Spilling1,2, Xujiong Ye3, Thomas Richard Barrick1, and Franklyn Arron Howe1
1St George's, University of London, London, United Kingdom, 2King's College London, London, United Kingdom, 3University of Lincoln, Lincoln, United Kingdom


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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