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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Modeling the MRI gradient system with a temporal convolutional network: Improved reconstruction by prediction 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, Nashville, Tennessee, USA.

Magnetic Resonance in Medicine

|August 19, 2025

Summary

This study introduces a new convolutional neural network model to accurately predict nonlinear gradient distortions in MRI. This advanced method improves image quality and diffusion parameter mapping, outperforming traditional techniques.

Keywords:

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

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Area of Science:

Medical Imaging
Machine Learning
Magnetic Resonance Imaging

Background:

Gradient system nonlinearities introduce distortions in MRI data.
Accurate modeling of these distortions is crucial for quantitative imaging.
Existing linear methods have limitations in capturing complex nonlinear behaviors.

Purpose of the Study:

To develop a general, nonlinear gradient system model using convolutional networks.
To accurately predict gradient distortions in magnetic resonance imaging (MRI).

Main Methods:

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

Main Results:

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

Conclusions:

Temporal convolutional networks offer superior modeling of gradient system behavior compared to linear methods.
TCNs can be utilized for retrospective correction of gradient errors in MRI.
This approach enhances the accuracy of quantitative MRI techniques.