Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac $T

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Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac $T_1$ Mapping

Published:2020 Issue: Volume:6 Page:194-207
ISSN:2333-9403
Container-title:IEEE Transactions on Computational Imaging
language:
Short-container-title:IEEE Trans. Comput. Imaging

Author:

Yaman Burhaneddin^ORCID,Weingartner Sebastian,Kargas Nikolaos^ORCID,Sidiropoulos Nicholas D.^ORCID,Akcakaya Mehmet^ORCID

Funder

NIH

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Mathematics,Computer Science Applications,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/6745852/8977810/8834863-aam.pdf

Cited by 33 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mixed norm regularized models for low-rank tensor completion;Information Sciences;2024-06

2. Tensor Methods in Biomedical Image Analysis;Journal of Medical Signals & Sensors;2024-06

3. Alternating low‐rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking;Magnetic Resonance in Medicine;2024-05-10

4. Fast T1 mapping MRI in preclinical and clinical settings using subspace-constrained joint-domain reconstructions;Magnetic Resonance Letters;2024-04

5. T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction;Computers in Biology and Medicine;2024-03

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