Related Experiment Video
Updated: Jan 17, 2026
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
Published on: August 14, 2019
Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of
Jonathan B Martin1, Hannah E Alderson1,2, John C Gore1,2,3
1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Tennessee, USA.
Abstract:
Errors in gradient trajectories introduce significant artifacts and distortions in magnetic resonance images, particularly in non-Cartesian imaging sequences, where imperfect gradient waveforms can greatly reduce image quality.
Purpose:
Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks.
Methods:
A set of training gradient waveforms were measured on a small animal imaging system, and used to train a temporal convolutional network to predict the gradient waveforms produced by the imaging system.
Results:
The trained network was able to accurately predict nonlinear distortions produced by the gradient system. Network prediction of gradient waveforms was incorporated into the image reconstruction pipeline and provided improvements in image quality and diffusion parameter mapping compared to both the nominal gradient waveform and the gradient impulse response function.
Conclusion:
Temporal convolutional networks can more accurately model gradient system behavior than existing linear methods and may be used to retrospectively correct gradient errors.
