Valsamis Jake J, Dubovan Paul I, Baron Corey A
Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada.
Center for Functional and Metabolic Mapping, Robarts Research Institute, London, Ontario, Canada.
Magn Reson Med. 2022 May;87(5):2209-2223. doi: 10.1002/mrm.29124. Epub 2021 Dec 11.
To develop and test a method for reducing artifacts due to time-varying eddy currents in oscillating gradient spin-echo (OGSE) diffusion images.
An in-house algorithm (TVEDDY), that for the first time retrospectively models eddy current decay, was tested on pulsed gradient spin echo and OGSE brain images acquired at 7 T. Image pairs were acquired using opposite polarity diffusion gradients. A three-parameter exponential decay model (two amplitudes and a time constant) was used to characterize and correct eddy current distortions by minimizing the intensity difference between image pairs. Correction performance was compared with conventional correction methods by evaluating the mean squared error (MSE) between diffusion-weighted images acquired with opposite polarity diffusion gradients. As a ground-truth comparison, images were corrected using field dynamics up to third order in space, measured using a field monitoring system.
Time-varying eddy currents were observed for OGSE, which introduced blurring that was not reduced using the traditional approach but was diminished considerably with TVEDDY and field monitoring-informed model-based reconstruction. No MSE difference was observed between the conventional approach and TVEDDY for pulsed gradient spin echo, but for OGSE TVEDDY resulted in significantly lower MSE than the conventional approach. The field-monitoring reconstruction had the lowest MSE for both pulsed gradient spin echo and OGSE.
This work establishes that it is possible to estimate time-varying eddy currents from the actual diffusion data, which provides substantial image-quality improvements for gradient-intensive diffusion MRI acquisitions like OGSE.
开发并测试一种用于减少振荡梯度自旋回波（OGSE）扩散图像中时变涡流伪影的方法。
在7T下采集的脉冲梯度自旋回波和OGSE脑图像上测试了一种内部算法（TVEDDY），该算法首次对涡流衰减进行回顾性建模。使用相反极性的扩散梯度采集图像对。采用三参数指数衰减模型（两个幅度和一个时间常数），通过最小化图像对之间的强度差异来表征和校正涡流畸变。通过评估使用相反极性扩散梯度采集的扩散加权图像之间的均方误差（MSE），将校正性能与传统校正方法进行比较。作为地面真值比较，使用场监测系统测量的空间中高达三阶的场动态来校正图像。
观察到OGSE存在时变涡流，其引入的模糊现象使用传统方法无法减少，但使用TVEDDY和基于场监测信息的模型重建可显著减少。对于脉冲梯度自旋回波，传统方法和TVEDDY之间未观察到MSE差异，但对于OGSE，TVEDDY导致的MSE明显低于传统方法。场监测重建在脉冲梯度自旋回波和OGSE中均具有最低的MSE。
这项工作表明，可以从实际扩散数据中估计时变涡流，这为像OGSE这样的梯度密集型扩散MRI采集提供了显著的图像质量改善。
