Deep-learning/machine-learning based super-resolution techniques have shown promises in improving the resolution of MRI without additional acquisition. In this study, we examined the capability of deep-learning based super-resolution using a newly developed network at resolutions from 0.2 mm to 0.025 mm. We also investigated whether the networks were able to enhance data acquired with a different contrast. Our results demonstrated that the enhancement of deep learning based super-resolution, although better than cubic interpolation, remained limited. In order to achieve the best performance, the network needs to be trained using data acquired at the target resolution and share similar contrasts.
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