The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited by physical constraints. To visualize metabolic processes of small structures using CEST in patients knees, an increased resolution is necessary. In this work, we compared trilinear interpolation and zero-filling to neural network-based approaches to estimate a high-resolution image given the corresponding low-resolution data. We could show that a substantial quantitative improvement using neural networks could be achieved for unsaturated images while maintaining a comparable CEST contrast. Generalization of the method to brain CEST MRI was achieved without retraining of the network.
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