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Related Experiment Video

Updated: Jan 17, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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 Alderson^1,2, John C Gore^1,2,3

¹Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Tennessee, USA.

|September 22, 2025

Summary

This summary is machine-generated.

Temporal convolutional networks accurately predict magnetic resonance imaging gradient distortions. This method improves image quality and diffusion parameter mapping, offering a better alternative to linear models for correcting gradient errors.

Keywords:

diffusion MRI gradient correction image reconstruction machine learning noncartesian trajectory temporal convolutional network time series forecasting

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Related Experiment Videos

Last Updated: Jan 17, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Medical Imaging
Magnetic Resonance Imaging (MRI)
Computational Imaging

Background:

Gradient trajectory errors cause artifacts in MRI, especially in non-Cartesian sequences.
Imperfect gradient waveforms significantly degrade image quality.

Purpose of the Study:

Develop a general, nonlinear gradient system model.
Accurately predict gradient distortions using convolutional networks.

Main Methods:

Measured gradient waveforms on a small animal MRI system.
Trained a temporal convolutional network (TCN) to predict gradient waveforms.
Integrated TCN predictions into the image reconstruction pipeline.

Main Results:

The TCN accurately predicted nonlinear gradient system distortions.
Incorporating TCN predictions improved image quality and diffusion parameter mapping.
Performance surpassed nominal waveforms and gradient impulse response functions.

Conclusions:

TCNs offer more accurate modeling of gradient system behavior than linear methods.
TCNs can be used for retrospective correction of gradient errors.
This approach enhances MRI image quality and quantitative parameter accuracy.