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Abstract #0365

Improving k-t PERRI: a low-rank data-driven fMRI k-t acceleration method

Harry T Mason1, Karla L Miller1, Nadine Graedel2, and Mark Chiew1

1Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom, 2Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, United Kingdom

FMRI data quality requires both good image fidelity (conferring spatial specificity), and high temporal resolution (conferring statistical robustness). We present an image reconstruction algorithm that aims to achieve both aims through a spatio-temporal (k-t) image reconstruction. Our approach utilises low-rank reconstruction algorithms and 3D golden angle k-space sampling. Using golden-angle sampling, we demonstrate that data-driven spatial and temporal priors can be incorporated into reconstruction. We demonstrate improvement over previously-proposed methods (k-t FASTER and k-t PSF) that correspond to special cases of our prior-based reconstruction. These results have great potential to improve on time-independent reconstructions currently in use.

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