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Abstract #3295

Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Distortions

Jonathan B Martin1, Hannah E Alderson1, John C Gore1, Mark D Does1, and Kevin D Harkins1
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States

Synopsis

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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Keywords