Data-driven reconstruction of undersampled raw data has gained more and more importance in recent years. Due to the non-linear and non-stationary transform characteristics of these imaging methods, objective image quality assessment is difficult. We propose a heuristic approach based on local point spread functions and multiple replica reconstructions, to enable the derivation of resolution- and g-factor-maps for individual images. The method is exemplarily applied in T1- and T2-weighted images of the brain, using a UNet and a Variational Network trained with data from the fastMRI project.
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