Phantom-Based Iterative Estimation of MRI Gradient Nonlinearity
Joshua Trzasko 1 , Shengzhen Tao 1 , Jeffrey Gunter 1 , Yunhong Shu 1 , John Huston III 1 , and Matt Bernstein 1
Mayo Clinic, Rochester, MN, United States
Gradient nonlinearity (GNL) correction is a standard
process performed on MRI scanners to eliminate geometric
spatial distortion that arises from imperfect hardware
performance. Typically, the gradient field is estimated
via electromagnetic (EM) simulation for a scanner type,
but does not account for scanner-specific variations due
to hardware construction (e.g., winding) or siting.
Recently, a phantom-based calibration procedure was
developed that enables accurate individual field
estimation without needing proprietary information. In
this work, we develop a new iterative estimation
strategy based on post-GNL correction distortion mean
square error (MSE) minimization that further improves
scanner-specific gradient field estimation accuracy.
This abstract and the presentation materials are available to members only;
a login is required.