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Deep-learning-based image reconstruction shows considerable promise to accelerate both static and dynamic MR imaging and to address imaging artifacts including aliasing, motion, and ghosting.
- PubMed
This article is an introductory overview aimed at clinical...
- PubMed
22 de mar. de 2018 · Here we present a unified framework for image reconstruction—automated transform by manifold approximation (AUTOMAP)—which recasts image reconstruction as a data-driven supervised...
1 de jul. de 2021 · To accelerate MR scan, three mainstream methods have been developed, namely, physics based fast imaging sequences, hardware based parallel imaging with multiple coils and signal processing based MR image reconstruction from incomplete k-space data.
5 de dic. de 2017 · In this work, we propose a unique, novel convolutional recurrent neural network (CRNN) architecture which reconstructs high quality cardiac MR images from highly undersampled k-space data by jointly exploiting the dependencies of the temporal sequences as well as the iterative nature of the traditional optimisation algorithms.
12 de sept. de 2018 · We have presented a deep learning based MR imaging reconstruction method, wherein real-valued neural network operations are replaced by complex convolutional operations.
1 de nov. de 2010 · In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure.
