Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction
Model driven deep learning reconstruction methods usually utilize residual learning within single cascade which is composed by a neural network and a data consistency module, here we propose a model based end-to-end residual learning variational network(E2E-ResVarNet), a k-space residual is passed through cascades, then added to acquired under-sampled k-space after the last cascade output. It was demonstrated that the image quality is significantly improved at 4x/6x/8x acceleration factors trained with brain data set.
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