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Abstract #5219

Gradient Non-Linearity Calibration for Improved Spatial Position Accuracy with a High-Performance Gradient Using a Fiducial Phantom

Nastaren Abad1, Eric Fiveland1, Seung-Kyun Lee1, Yihe Hua1, Shengzhen Tao2, Joshua Trazasko3, Matt A. Bernstein3, and Thomas K.F Foo1
1GE Research, Niskayuna, NY, United States, 2Mayo Clinic, Jacksonville, FL, United States, 3Mayo Clinic, Rochester, MN, United States

Synopsis

Keywords: System Imperfections: Measurement & Correction, System Imperfections: Measurement & Correction, Gradient Non-Linearity; Spatial Fidelity; High Performance GradientsSpatial encoding in MR-systems is subject to gradient non-linearity. If ignored or inadequately calibrated during system install, non-linear gradient fields manifest as spatial distortions impacting image quality, diminish accuracy for applications requiring MR guided intervention and introduce systematic errors in quantitative imaging such as diffusion MRI. High-performance, high-efficiency gradient systems, such as MAGNUS, require accurate calibration for imaging precision. In this study, a fiducial phantom was utilized to characterize and correct for residual distortions after standard gradient calibrations. Results highlight that distortion due to gradient non-linearity can be successfully reduced by phantom-based calibration for improved accuracy inline with QC metrics.

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