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Abstract #0835

Regularized Second-order Dynamic Shimming

Yuhang Shi1, S. Johanna Vannesjo1, and Stuart Clare1

1Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom

The implementation of dynamic shimming relies on determining robust and accurate shim currents. This work presents a novel, analytical, and fully automated regularized shim determination technique to solve ill-conditioned least-square problems and regularize current constraint challenges for dynamic shimming applications. The method is based on the Tikhonov regularization whereby the L-curve method is utilized to search for an optimal shim solution. The method improves shim current use efficiency and conditioning of the shim determination problem, outperforming the truncated singular value decomposition regularization based least-square method.

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