This work focused on accelerating DTI using deep learning methods. Three neural networks including U-net, PD-net and Cascade-net were investigated on reconstructing DTI images, ADC maps and FA maps from Cartesian under-sampled k-space data. The results indicated that Cascade-net out-performed the other two networks, obtaining comparable image quality as compared with the reference reconstructed from full k-space data. In summary, neural networks can be used to accelerate DTI while maintaining image quality.
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