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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