Martin Jonathan B, Alderson Hannah E, Gore John C, Does Mark D, Harkins Kevin D
Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Magn Reson Med. 2025 Aug 18. doi: 10.1002/mrm.70044.
Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks.
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.
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.
Temporal convolutional networks can more accurately model gradient system behavior than existing linear methods and may be used to retrospectively correct gradient errors.
我们的目标是开发一种通用的非线性梯度系统模型，该模型能够使用卷积网络准确预测梯度失真。
在小动物成像系统上测量了一组训练梯度波形，并用于训练时间卷积网络，以预测成像系统产生的梯度波形。
训练后的网络能够准确预测梯度系统产生的非线性失真。梯度波形的网络预测被纳入图像重建流程，与标称梯度波形和梯度脉冲响应函数相比，在图像质量和扩散参数映射方面均有改善。
与现有的线性方法相比，时间卷积网络能够更准确地模拟梯度系统行为，并且可用于回顾性校正梯度误差。
