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Abstract #3735

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

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.

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