Optimized Parallel Combination of Deep Networks and Sparsity Regularization for MR Image Reconstruction (OPCoNS)
Avrajit Ghosh1, Shijun Liang2, Anish Lahiri 3, and Saiprasad Ravishankar 1,2
1Department of Computational Mathematrics Science and Engineering, Michigan State University, East Lansing, MI, United States, 2Department of Biomedical Engineering, Michigan State University, East Lansing, MI, United States, 3Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States
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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