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, Tennessee, USA.
Department of Biomedical Engineering, Vanderbilt University, Tennessee, USA.
ArXiv. 2025 Jun 17:arXiv:2506.14995v1.
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.
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.
梯度轨迹中的误差会在磁共振图像中引入显著的伪影和失真，尤其是在非笛卡尔成像序列中，不完善的梯度波形会大大降低图像质量。
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都有改进。
与现有的线性方法相比，时间卷积网络可以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。
