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Abstract #2255

Faster Convergence for CS-SENSE Reconstruction

Mariya Doneva1, Peter Brnert1

1Philips Research Europe, Hamburg, Germany

Fast reconstruction is crucial for the implementation of CS-SENSE on clinical scanners. Thus, improvements of the reconstruction speed both in terms of algorithms with improved convergence and parallel implementation are desirable.

Keywords

accelerated according achieved achieves acts adapted adjusted adjusting almost alternatively applied backtracking becomes best clinical coefficients coil combination combined compressed computations conditioning conjugate constrained contain containing convergence crucial denotes density desirable determine determined determines diagonal directly eigenvalue erroneous estimation even fast finest formulated fully function furthermore generally geometry gradient gradients greatly guarantees hamburg head ideal implementation improved improvement improvements in vivo inaccuracies incoherent individual influence integrated interpretation involved issue iteration iterations iterative king linear maps matrices matrix measured median minimization modified noisy normalized operator optimal parallel pattern phantom pixel potential power practical previous problem problematic prolong properties propose proposed random recently reconstructed reconstruction recovered recovers reduce reduced reduction reformulate remain resolution respect restricted robustness sampled sampling scale scanner scheme search sense sensing sensitive sensitivities sensitivity simulated since soft solution solve soviet space sparsity speed squared squares step still substitute suggests support synthesis synthetic terms threshold thresholding transform unchanged unconstrained update variable vector wavelet