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基于梯度系统传递函数的梯度波形预加重。

Gradient waveform pre-emphasis based on the gradient system transfer function.

机构信息

Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany.

X-Ray & Molecular Imaging Laboratory, OTH - Technical University of Applied Sciences, Weiden, Germany.

出版信息

Magn Reson Med. 2018 Oct;80(4):1521-1532. doi: 10.1002/mrm.27147. Epub 2018 Feb 25.

Abstract

PURPOSE

The gradient system transfer function (GSTF) has been used to describe the distorted k-space trajectory for image reconstruction. The purpose of this work was to use the GSTF to determine the pre-emphasis for an undistorted gradient output and intended k-space trajectory.

METHODS

The GSTF of the MR system was determined using only standard MR hardware without special equipment such as field probes or a field camera. The GSTF was used for trajectory prediction in image reconstruction and for a gradient waveform pre-emphasis. As test sequences, a gradient-echo sequence with phase-encoding gradient modulation and a gradient-echo sequence with a spiral read-out trajectory were implemented and subsequently applied on a structural phantom and in vivo head measurements.

RESULTS

Image artifacts were successfully suppressed by applying the GSTF-based pre-emphasis. Equivalent results are achieved with images acquired using GSTF-based post-correction of the trajectory as a part of image reconstruction. In contrast, the pre-emphasis approach allows reconstruction using the initially intended trajectory.

CONCLUSION

The artifact suppression shown for two sequences demonstrates that the GSTF can serve for a novel pre-emphasis. A pre-emphasis based on the GSTF information can be applied to any arbitrary sequence type.

摘要

目的

梯度系统传递函数(GSTF)已被用于描述图像重建中的失真 k 空间轨迹。本研究旨在使用 GSTF 确定无失真梯度输出和预期 k 空间轨迹的预加重。

方法

仅使用标准磁共振硬件确定 MR 系统的 GSTF,无需特殊设备(如场探头或场相机)。GSTF 用于图像重建中的轨迹预测和梯度波形预加重。作为测试序列,实现了相位编码梯度调制的梯度回波序列和螺旋读取轨迹的梯度回波序列,随后在结构体模和体内头部测量中进行了应用。

结果

通过应用基于 GSTF 的预加重成功抑制了图像伪影。使用 GSTF 作为图像重建一部分对轨迹进行后校正获得的图像可获得等效结果。相比之下,预加重方法允许使用最初预期的轨迹进行重建。

结论

两种序列的伪影抑制表明,GSTF 可用于新的预加重。基于 GSTF 信息的预加重可应用于任何任意序列类型。

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