Recently, deep learning models have been developed for reconstructing data acquired using radial turbo spin echo sequences, yielding images at multiple echo times as well as co-registered T2 maps. In radial imaging, streaking artifacts from anatomical regions where the magnetic field gradients are nonlinear can obscure pathology and impact accuracy of parameter mapping. In this work, we demonstrate that removal of streaking artifacts from data prior to training can provide substantial improvement in the reconstruction performance of deep learning methods.
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