Abstract:
k-space preconditioning accelerates iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data.
Reconstruction accuracy is sacrificed by sampling density compensations or circulant preconditioners. We fix both issues. We demonstrate that the dual formulation of the reconstruction problem allows density-compensation-like preconditioning in k-space.
The primal-dual hybrid gradient method is used to precondition using no inner loops and accelerate convergence faster than existing algorithms. We derive _2-optimized preconditioners and show through experiments that the proposed method converges in about ten iterations.
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