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.
Originalsprache | englisch |
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Seiten (von - bis) | 1646-1654 |
Fachzeitschrift | IEEE Transactions on Medical Imaging |
Jahrgang | 39 |
Ausgabenummer | 5 |
DOIs | |
Publikationsstatus | Veröffentlicht - Mai 2020 |
Extern publiziert | Ja |
Fields of Expertise
- Human- & Biotechnology
- Information, Communication & Computing