Computer-implemented method of training a machine learning based processing unit, preferably a Neural network, NN, and most preferably a U-net, comprising the steps: - receiving a training set of multiple training signal data sets of a plurality of spin echo sequences; - receiving an associated set of ground truth image data with increased quality in terms of resolution, artefacts and/or contrast compared to the training set; - iteratively providing the training signal data sets of the received training set to the processing unit; - iteratively adapting internal behaviour of the processing unit, preferably weights of the NN and most preferably weights of the U-net based on a comparison of image data derived by the processing unit based on the current provided training signal data set and a current internal behaviour of the processing unit with the associated ground truth image data - receiving a further training set of supplemental data sets of the plurality of spin echo sequences including at least one of B 0 -maps, reverted phase encoding directions and distortion information associated to the multiple training signal data sets; - iteratively providing the supplemental data sets associated to the multiple training signal data sets to the processing unit, wherein, in the step of adapting, the image data is derived by the processing unit based on the current provided training signal data set together with the associated supplemental data set and the current internal behaviour of the processing unit, and wherein the machine learning based processing unit is trained for deriving refined image data with a refined image quality from received signal data sets and supplemental data sets of a plurality of spin echo sequences from the same subject, corresponding to the plurality of spin echo sequences of the training set and the further training set, and the supplemental data sets including at least one of B0-maps, reverted phase encoding directions and distortion information associated with the received signal data sets. - receiving a training set of multiple training signal data sets of a plurality of spin echo sequences; - receiving an associated set of ground truth image data with increased quality in terms of resolution, artefacts and/or contrast compared to the training set; - iteratively providing the training signal data sets of the received training set to the processing unit; - iteratively adapting internal behaviour of the processing unit, preferably weights of the NN and most preferably weights of the U-net based on a comparison of image data derived by the processing unit based on the current provided training signal data set and a current internal behaviour of the processing unit with the associated ground truth image data - receiving a further training set of supplemental data sets of the plurality of spin echo sequences including at least one of B 0 -maps, reverted phase encoding directions and distortion information associated to the multiple training signal data sets; - iteratively providing the supplemental data sets associated to the multiple training signal data sets to the processing unit, wherein, in the step of adapting, the image data is derived by the processing unit based on the current provided training signal data set together with the associated supplemental data set and the current internal behaviour of the processing unit, and wherein the machine learning based processing unit is trained for deriving refined image data with a refined image quality from received signal data sets and supplemental data sets of a plurality of spin echo sequences from the same subject, corresponding to the plurality of spin echo sequences of the training set and the further training set, and the supplemental data sets including at least one of B0-maps, reverted phase encoding directions and distortion information associated with the received signal data sets. wherein, in the step of adapting, the image data is derived by the processing unit based on the current provided training signal data set together with the associated supplemental data set and the current internal behaviour of the processing unit, and wherein the machine learning based processing unit is trained for deriving refined image data with a refined image quality from received signal data sets and supplemental data sets of a plurality of spin echo sequences from the same subject, corresponding to the plurality of spin echo sequences of the training set and the further training set, and the supplemental data sets including at least one of B0-maps, reverted phase encoding directions and distortion information associated with the received signal data sets.