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Abstract #3065

Super-resolution Hyperpolarized C13 Imaging with 2D-Linear Prediction and Trigonometric Curves

Jack J J J Miller1,2,3, Sofia Dimoudi1, Aaron Hess1, and Damian J Tyler1,3

1Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, United Kingdom, 2Department of Physics, University of Oxford, Oxford, United Kingdom, 3Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

Hyperpolarized $$$^{13}\text{C}$$$-imaging techniques a powerful and clinically translatable method to image metabolism. However, owing to the finite and non-renewable magnetisation available to the technique, all proposed imaging sequences necessarily have a comparatively small matrix size compared to conventional anatomical imaging. Typically hyperpolarized images are therefore reconstructed with a large degree of zero-filling. We show here that a modified form of 2D least-squares linear prediction that uses the known analytic properties of trigonometric curves can extrapolate unmeasured Fourier coefficients and hence improve the apparent reconstructed resolution of hyperpolarized images.

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