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Abstract #5163

Improved Parallel Imaging with Resilience to Gradient Errors

Gigi Galiana1 and Nadine Luedicke2

1Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 2Biomedical Engineering, Yale University, New Haven, CT, United States

FRONSAC encoding, which adds a rapidly oscillating low-amplitude nonlinear gradient to a standard undersampled trajectory based on linear gradients, has been shown to significantly improve k-space coverage and parallel imaging reconstructions. This abstract further shows that a fixed FRONSAC waveform improves image quality for Cartesian trajectories of various FOV and resolution, while avoiding many of the pitfalls of other highly efficient gradient trajectories. Results show that FRONSAC provides better reconstruction than Cartesian encoding alone, while offering better resilience to delays and off-resonance effects than non-Cartesian trajectories, such as spiral.

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