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Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning.


ABSTRACT: We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density compensations which sacrifice reconstruction accuracy, or circulant preconditioners which increase per-iteration computation. Our approach overcomes both shortcomings. Concretely, we show that viewing the reconstruction problem in the dual formulation allows us to precondition in k-space using density-compensation-like operations. Using the primal-dual hybrid gradient method, the proposed preconditioning method does not have inner loops and are competitive in accelerating convergence compared to existing algorithms. We derive l2 -optimized preconditioners, and demonstrate through experiments that the proposed method converges in about ten iterations in practice.

SUBMITTER: Ong F 

PROVIDER: S-EPMC7285911 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning.

Ong Frank F   Uecker Martin M   Lustig Michael M  

IEEE transactions on medical imaging 20191119 5


We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data. Existing methods either use sampling density compensations which sacrifice reconstruction accuracy, or circulant preconditioners which increase per-iteration computation. Our approach overcomes both shortcomings. Concretely, we show that viewing the reconstruction problem in the dual formulation allows us to preco  ...[more]

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