This work examines optimized parallel combinations of deep networks and conventional regularized reconstruction for improved quality of MR image reconstructions from undersampled k-space data. Features learned by deep networks and typical model-based iterative algorithms (e.g., sparsity-penalized reconstruction) could complement each other for effective reconstructions. We observe that combining the image features from multiple approaches in a parallel fashion with appropriate learned weights leads to more effective image representations that are not captured by either strictly supervised or (unsupervised) conventional iterative methods.
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