Martin, Jonathan B., Alderson, Hannah E., Gore, John C., Does, Mark D., & Harkins, Kevin D. (2025). Modeling the MRI gradient system with a temporal convolutional network: Improved reconstruction by prediction of readout gradient errors. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.70044
The goal of this study was to develop a model that can better predict and correct distortions in MRI images caused by imperfections in the gradient system, which is responsible for shaping the magnetic fields used to create images. To do this, we trained a temporal convolutional network—a type of machine learning model—using data collected from a small animal imaging system. This network learned to predict the actual gradient waveforms produced by the scanner, including the nonlinear distortions that often reduce image quality.
When we incorporated these predictions into the image reconstruction process, the results showed clearer images and more accurate mapping of diffusion parameters compared to standard methods. This means that our approach outperformed both the use of the system’s default gradient settings and the commonly used gradient impulse response function. Overall, this work shows that temporal convolutional networks provide a more accurate way to model gradient behavior and can be used to correct errors after scanning, ultimately improving the quality and reliability of MRI images.
FIGURE 1
Spiral and chirp gradient waveforms measured on the 7T Bruker system, with conspicuous nonlinearities. Waveform timecourses are normalized by their respective maximum nominal amplitudes. (A,B) show the nominal waveforms with zoomed-in ROI highlighted. (C,D) show the nominal and measured waveforms at several different amplitudes. Clear nonlinearities are present. In (C) the two waveforms are not simply scaled copies of one another but have distinct zero crossing artifacts. In (D), the response varies with amplitude of the applied waveform. Delay and attenuation increase with decreasing amplitude