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Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors.

, , , , , and . IEEE Trans. Medical Imaging, 35 (9): 2119-2129 (2016)

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Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors., , , , , and . IEEE Trans. Medical Imaging, 35 (9): 2119-2129 (2016)Accelerating Cardiovascular Magnetic Resonance Imaging: Signal Processing Meets Nuclear Spins Life Sciences., , and . IEEE Signal Process. Mag., 31 (5): 138-143 (2014)Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network., , , , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 91-99. Springer, (2018)Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints., , , , , , , and . IEEE Trans. Biomed. Eng., 65 (10): 2219-2230 (2018)Four-dimensional MR cardiovascular imaging: Method and applications., , , , and . EMBC, page 3732-3735. IEEE, (2011)Improved Subspace Estimation for Low-Rank Model-Based Accelerated Cardiac Imaging., , , , and . IEEE Trans. Biomed. Eng., 61 (9): 2451-2457 (2014)Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction., , , , and . ISBI, page 1-5. IEEE, (2022)Brain MRI super resolution using 3D deep densely connected neural networks., , , , , and . ISBI, page 739-742. IEEE, (2018)Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints., , , and . ISBI, page 1593-1596. IEEE, (2011)Image Reconstruction From Highly Undersampled ( k, t) -Space Data With Joint Partial Separability and Sparsity Constraints., , , and . IEEE Trans. Med. Imaging, 31 (9): 1809-1820 (2012)