Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 39, Issue 5, Pages 1646-1654Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2954121
Keywords
Image reconstruction; Convergence; Magnetic resonance imaging; Gradient methods; Acceleration; Trajectory; MRI; iterative reconstruction; non-cartesian; preconditioner; density compensation
Categories
Funding
- NIH [R01EB009690]
- Sloan Research Fellowship
- Bakar Fellowship
- GE Healthcare
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available