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Publication Detail
Joint reconstruction of low-rank and sparse components from undersampled (k, t)-space small bowel data
  • Publication Type:
  • Authors:
    Dikaios N, Tremoulheac B, Menys A, Hamy V, Arridge S, Atkinson D
  • Publication date:
  • Published proceedings:
    Nuclear science symposium and medical imaging conference
  • ISBN-13:
  • Status:
  • Name of conference:
  • Conference place:
    Seoul, Korea
  • Conference start date:
  • Conference finish date:
Quantification of small bowel motility is a potential marker of disorders and assessment of response to therapy. MR imaging is a non-invasive diagnostic tool that can depict small bowel motion. Adequate temporal resolution and coverage is important for accurate estimation of small bowel motility. Compressed sensing exploits the expected sparsity in a transform domain and can reconstruct randomly undersampled k-space data, thus significantly accelerating the MR acquisition. A non linear reconstruction is required to promote the sparsity while maintaining the consistency with the acquired data. An alternative sparse domain is the singular values of a matrix and this can be promoted using low rank. In this work an adaptation of the split Bregman reconstruction is used to recover low rank and sparse components from undersampled dynamic 3D data. Simulated datasets are used to compare different undersampling factors. The proposed method improved the correspondence (based on mutual information) to the fully sampled reconstructed image up to 33% compared to the Fourier transformation of the zero filled sampling points for an undersampling factor of 4
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