Images from a multiparameter mapping sequence were reconstructed with a novel deep learning based reconstruction (DL Recon) method trained to remove noise and enhance edges. Mean T1, T2, and PD values as measured in a system phantom differed by less than 0.6% between the DL and conventional reconstructions, while noise was lower in all measurements on DL Recon images. In vivo synthetic images also exhibited reduced noise and increased definition of structures. We find that the SNR and resolution benefits of DL Recon applied to raw MR data extend to improve the fitted relaxation maps and subsequent synthetic images.
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