Simultaneous Estimation of Auto-calibration Data and Gradient Delays in non-Cartesian Parallel MRI using Low-rank Constraints
Wenwen Jiang1, Peder E.Z Larson2, and Michael Lustig3
1Bioengineering, UC Berkeley/ UCSF, Berkeley, CA, United States, 2Radiology and Biomedical Imaging, UCSF, San francisco, CA, United States, 3Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA, United States
Gradient timing delay errors in non-Cartesian
trajectories often induce spurious image artifacts. More importantly, misaligned
k-space center data results in auto-calibration errors for parallel imaging
methods. We propose a general approach that simultaneously estimates consistent
calibration data and corrects for gradient delays. We pose the joint estimation
problem as a low-rank minimization problem, and solve it using a Gauss-Newton
method. We demonstrate the feasibility of the proposed method by simulation and
phantom experiments.
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