Keywords: Gradients, Machine Learning/Artificial Intelligence
Motivation: Nonlinear gradient trajectory errors make imaging and parameter mapping challenging, especially in noncartesian imaging sequences. In many cases severe distortions dramatically impact image quality.
Goal(s): To develop a nonlinear model that can accurately predict gradient distortions.
Approach: We use a temporal convolutional network trained on measured gradient waveforms to predict gradient system outputs, and incorporate these predictions into image reconstruction.
Results: Using the nonlinear TCN model results in improved image quality and diffusion parameter estimation over linear system models in a multishot imaging sequence.
Impact: Nonlinear gradient errors do dramatically impact image quality but may be remediated with an accurate nonlinear model. Having a more accurate model of gradient distortions may allow for greater flexibility in the gradient waveforms used in MRI.
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