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Abstract #4386

Discrete shearlets as a sparsifying transform in a split Bregman reconstruction of low-rank plus sparse component from undersampled (k, t)-space small bowel data

Nikolaos Dikaios 1 , Benjamin Tremoulheac 1 , Alex Menys 2 , Simon Arridge 1 , and David Atkinson 2

1 Dept. of Medical Physics and Bioengineering, University College London, London, Great London, United Kingdom, 2 Centre of Medical Imaging, University College London, Great London, United Kingdom

Quantification of small bowel motility correlates with disorders such as Crohns disease. The motility metric can be derived from non rigid registration of dynamic MR images, and its accuracy depends on their spatial/temporal resolution. We propose a split Bregman algorithm to reconstruct alias free dynamic MR images from undersampled (k,t)-space data, improving either the temporal resolution or maintaining the same temporal resolution and improving the spatial resolution. The proposed algorithm uses shearlets as an optimal sparsifying transform, and assumes that the recovered image consists of a low-rank plus a sparse component.

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