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  • Title: ➤  Assessing The Influence Of Degradation Functions In Deep Learned Super-resolution Of Brain Magnetic Resonance Images.
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Super-resolution in magnetic resonance imaging can shorten the scanning time whilst retaining resolution or can increase resolution whilst retaining the same scanning time. The most used method for super resolution in recent years is a deep-learned approach. Training a deep-learned super-resolution model typically involves scanning high-resolution images, artificially down sampling these images to obtain low resolution images and letting the model learn the best transform from low-resolution to high-resolution images. These artificial down sampling methods most often involve blurring and down sampling or truncating k-space. It remains an open question if these two methods resemble actual low-resolution MR acquisitions. To determine if the artificial methods are sufficient to mimic the actual low-resolution acquisition, we will acquire T2 weighted MR scans at multiple resolutions, apply the artificial down sampling methods to our high-resolution images and train models on both the artificially down-sampled datasets and the actual low-resolution acquisitions. Finally, we will assess if there are differences in model performance between the three down-sampling methods.

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