High-resolution (HR) quantitative magnetic resonance images (qMRI) are widely used in clinical diagnosis. However, acquisition of such high signal-to-noise ratio data is time consuming, and could lead to motion artifacts. Super-resolution (SR) approaches provide a better trade-off between acquisition time and spatial resolution. However, State-of-the-art SR methods are mostly supervised, which require external training data consisting of specific LR-HR pairs, and have not considered the quantitative conditions, which leads to the estimated quantitative map inaccurate. An self-supervised super-resolution algorithm under quantitative conditions is presented. Experiments on T1ρ quantitative images show encouraging improvements compared to competing SR methods.
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