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Abstract #3223

Revisiting adaptive regularization for self-calibrated, dynamic parallel imaging reconstruction

Mark Chiew1 and Karla L Miller1

1FMRIB, University of Oxford, Oxford, United Kingdom

In this work we demonstrate a simple method for reducing error in k-t under-sampled parallel imaging by subtracting a dynamic, low-rank time-series estimate prior to un-aliasing reconstruction. This estimate is generated directly from the under-sampled data by selecting the first $$$r$$$ components of a singular value decomposition after sliding-window reconstruction, and removes signal variance that might otherwise contribute to residual aliasing. This method is motivated by the observation that the highest variance components in time-series data are typically low-frequency, and well characterised by a sliding window filter.

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