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Abstract #3544

Accelerating Compressed Sensing in Cartesian Parallel Imaging Reconstructions using an Efficient and Effective Circulant Preconditioner

Jeroen van Gemert1, Kirsten Koolstra2, Peter Börnert3, Andrew Webb2, and Rob Remis1

1Circuits and Systems Group, Delft University of Technology, Delft, Netherlands, 2C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, Netherlands, 3Philips Research Hamburg, Hamburg, Germany

Reconstruction methods in parallel imaging and compressed sensing problems are generally very time consuming, especially for a large number of coil elements. In this work, the image is reconstructed using the Split Bregman algorithm (SB). We present an efficient and effective preconditioner that reduces the number of iterations in the linear least squares step of SB by almost a factor of 5 as alternative to extra variable splitting. The designed preconditioner works for Cartesian sampling schemes and for different coil configurations. It has negligible initialization time and leads to an overall speedup factor of 2.5.

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