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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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