Abstract #3695
Compressed sensing reconstruction of prospectively under-sampled cardiac diffusion tensor MRI
Darryl McClymont 1 , Irvin Teh 1 , Hannah Whittington 1 , and Jurgen Schneider 1
1
University of Oxford, Oxford, Oxfordshire,
United Kingdom
Compressed sensing offers a means to decrease the long
scan times of diffusion tensor MRI (DTI) by acquiring
only a subset of k-space. In this work, we present and
evaluate an algorithm for the reconstruction of
diffusion signals using data-driven dictionaries. Data
from one ex-vivo rat heart were prospectively
under-sampled with accelerations of two to five using a
novel k-space sampling scheme. Results indicate that
this approach is able to reconstruct DTI with minimal
compromise to image quality. To the authors knowledge,
this is the first study using compressed sensing to
reconstruct prospectively under-sampled cardiac DTI.
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