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In this work, we propose an iterative reconstruction scheme (ALONE - AdaptiveLearning Of NEtworks) for 2D radial cine MRI based on ground truth-freeunsupervised learning of shallow convolutional neural networks. The network istrained to approximate patches of the current estimate of the solution duringthe reconstruction. By imposing a shallow network topology and constraining the$L_2$-norm of the learned filters, the network's representation power islimited in order not to be able to recover noise. Therefore, the network can beinterpreted to perform a low dimensional approximation of the patches forstabilizing the inversion process. We compare the proposed reconstructionscheme to two ground truth-free reconstruction methods, namely a well knownTotal Variation (TV) minimization and an unsupervised adaptive DictionaryLearning (DIC) method. The proposed method outperforms both methods withrespect to all reported quantitative measures. Further, in contrast to DIC,where the sparse approximation of the patches involves the solution of acomplex optimization problem, ALONE only requires a forward pass of all patchesthrough the shallow network and therefore significantly accelerates thereconstruction.

Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI