Image Reconstruction Using a Gradient Impulse Response Model for Trajectory Prediction
Image Reconstruction Using a Gradient Impulse Response Model for Trajectory Prediction
复制标题
DOI：
10.1002/mrm.25841
复制
发表时间：
2016-07-01
影响因子：
3.3
通讯作者：
Pruessmann, Klaas P.
中科院分区：
文献类型：
--
作者：
Vannesjo, S. Johanna;Graedel, Nadine N.;Pruessmann, Klaas P.
目的：梯度不完美仍然是MRI中的一个挑战，特别是对于依赖于长成像读数的序列。本工作旨在探索基于梯度系统的脉冲响应模型预测的k空间轨迹的图像重建。理论与方法：梯度表征在商业3特斯拉（T）系统上进行两次，间隔3年。测量的梯度脉冲响应函数被用来预测实际的k空间轨迹的单次激发回波平面成像（EPI），螺旋和变速EPI序列。对体模和体内数据进行了基于预测轨迹的图像重建，并将所得图像与基于并行场监测、单独轨迹测量和标称轨迹的重建进行了比较。结果：使用基于模型的轨迹的图像重建产生了高质量的图像，与使用单独的轨迹测量相当。与使用标称轨迹相比，它大大减少了重影，模糊和几何失真。等效的图像质量与最近的表征，并进行3 years prior.Conclusion：基于模型的轨迹预测，使高质量的图像重建技术上具有挑战性的序列，如单次拍摄EPI和螺旋成像。因此，它为快速结构成像和先进的神经成像技术，包括功能磁共振成像，扩散张量成像和动脉自旋标记带来了巨大的希望。该方法可以基于一次性系统表征，如通过成功使用3年的校准数据所证明的。(C)2015 Wiley Periodicals，Inc.
Purpose: Gradient imperfections remain a challenge in MRI, especially for sequences relying on long imaging readouts. This work aims to explore image reconstruction based on k-space trajectories predicted by an impulse response model of the gradient system.Theory and Methods: Gradient characterization was performed twice with 3 years interval on a commercial 3 Tesla (T) system. The measured gradient impulse response functions were used to predict actual k-space trajectories for single-shot echo-planar imaging (EPI), spiral and variable-speed EPI sequences. Image reconstruction based on the predicted trajectories was performed for phantom and in vivo data.Resulting images were compared with reconstructions based on concurrent field monitoring, separate trajectory measurements, and nominal trajectories. Results: Image reconstruction using model-based trajectories yielded high-quality images, comparable to using separate trajectory measurements. Compared with using nominal trajectories, it strongly reduced ghosting, blurring, and geometric distortion. Equivalent image quality was obtained with the recent characterization and that performed 3 years prior.Conclusion: Model-based trajectory prediction enables high-quality image reconstruction for technically challenging sequences such as single-shot EPI and spiral imaging. It thus holds great promise for fast structural imaging and advanced neuroimaging techniques, including functional MRI, diffusion tensor imaging, and arterial spin labeling. The method can be based on a one-time system characterization as demonstrated by successful use of 3-year-old calibration data. (C) 2015 Wiley Periodicals, Inc.