Dynamic MR Image Reconstruction From Highly Undersampled (k, t)-Space Data Exploiting Low Tensor Train Rank and Sparse Prior

Dynamic MR Image Reconstruction From Highly Undersampled (k, t)-Space Data Exploiting Low Tensor Train Rank and Sparse Prior

Dynamic magnetic resonance imaging (dynamic MRI) is used to visualize living tissues and their changes over time. In this paper, we propose a new tensor-based dynamic MRI approach for reconstruction from highly undersampled (k, t)-space data, which combines low tensor train rankness and temporal spa...

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Bibliographic Details
Main Authors: Shuli Ma, Huiqian Du, Wenbo Mei
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Dynamic MR imaging
tensor train
sparsity
the alternating direction multiplier method
Online Access:https://ieeexplore.ieee.org/document/8986636/

Internet

https://ieeexplore.ieee.org/document/8986636/

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