期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 39, 期 5, 页码 1646-1654出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2954121
关键词
Image reconstruction; Convergence; Magnetic resonance imaging; Gradient methods; Acceleration; Trajectory; MRI; iterative reconstruction; non-cartesian; preconditioner; density compensation
类别
资金
- NIH [R01EB009690]
- Sloan Research Fellowship
- Bakar Fellowship
- GE Healthcare
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 $\ell 2$ -optimized preconditioners, and demonstrate through experiments that the proposed method converges in about ten iterations in practice.
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