The benefit of tree sparsity in accelerated MRI

@article{Chen2014TheBO,
  title={The benefit of tree sparsity in accelerated MRI},
  author={Chen Chen and Junzhou Huang},
  journal={Medical image analysis},
  year={2014},
  volume={18 6},
  pages={
          834-42
        }
}
The wavelet coefficients of a 2D natural image are not only approximately sparse with a large number of coefficients tend to be zeros, but also yield a quadtree structure. According to structured sparsity theory, the required measurement bounds for compressive sensing reconstruction can be reduced to O(K+log(N/K)) by exploiting the tree structure rather than O(K+Klog(N/K)) for standard K-sparse data. In this paper, we proposed two algorithms with convex relaxation to solve the tree-based MRI… CONTINUE READING
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An efficient algorithm for compressed mr imaging using total variation and wavelets

  • S. Ma, W. Yin, Y. Zhang, A. Chakraborty
  • Proceedings of CVPR.
  • 2008
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