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Dynamic MRI reconstruction exploiting blind compressed sensing combined transform learning regularization

  • Published:2020-06 Issue: Volume:392 Page:160-167
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

He Ning,Wang Ruolin,Wang Yixue

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference29 articles.

1. accelerated dynamic MRI using patch regularization for implicit motion compensation (PRICE);Mohsin;Magn. Reson. Imaging,2018

2. Dynamic MRI reconstruction from highly undersampled (k,t)-space data using weighted Schatten p-norm regularizer of tensor;Yang;Magn. Reson. Imaging,2017

3. Kit FOCUSS: a general compressed sensing framework for high resolution dynamic MRI;Jung;Magn. Reson. Imaging,2009

4. Low rank matrix recovery for real time cardiac MRI;Zhao,2010

5. Low-rank approximations for dynamic imaging;Haldar,2011

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2. ReconResNet: Regularised residual learning for MR image reconstruction of Undersampled Cartesian and Radial data;Computers in Biology and Medicine;2022-04

3. Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques;Current Medical Imaging Formerly Current Medical Imaging Reviews;2021-08

4. A Novel Image Restoration Method based on Iterative Algorithm;International Journal of Circuits, Systems and Signal Processing;2021-06-07

5. An Image Reconstruction Algorithm based on Sparse Representation for Image Compressed Sensing;International Journal of Circuits, Systems and Signal Processing;2021-06-04
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