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Image Quality Transfer with Auto-Encoding Applied to dMRI Super-Resolution

In recent years, diffusion-weighted magnetic resonance imaging (dMRI) has been widely applied in neurological examinations because it can reveal the microstructure of brains. The information extracted from dMRI are crucial for doctor in the diagnosis. However, the resolution of dMRI used in clinical practice usually cannot meet the need of diagnosis. Therefore, many technologies, such as machine learning are applied to improve the resolution of images. In this work, we studied the image super-resolution approach based on improved auto-encoder to reconstruct high-resolution images. Image quality transfer is regarded as the baseline, and high-frequency prior structural information are used as the features for training and evaluation of low and high-resolution samples. We compare our proposal with other competing algorithms on Human Connectome Project public data sets, and conduct subjective and objective evaluations. The results indicate that our proposed method outperforms the comparison algorithms.