论文标题

与周期性动力学的序列中涡流最小化

Minimization of Eddy Current Artifacts in Sequences with Periodic Dynamics

论文作者

Flassbeck, Sebastian, Assländer, Jakob

论文摘要

目的:将具有平衡梯度矩的周期性脉冲序列中的涡流最小化,例如用于定量MRI。 理论与方法:平衡序列中的涡流构造是由K空间中的大跳跃产生的。在定量MRI中,经常在获取K空间的不同部分时反复采样一些自旋动力学。我们在不同的重复之间交换单个K空间线,以最大程度地减少时间继承的跳跃而不改变整体轨迹。这种重新排序可以作为旅行推销员问题制定,我们通过模拟退火算法来解决离散优化。 结果:与默认排序相比,我们观察到重建图像和派生的定量参数图的伪影大幅降低。比较了我们的算法的两个变体,一种类似于Bieri等人最初提出的配对方法,而将所有K空间均匀跳跃最小化的一种变体,我们观察到后者的伪影水平略低。 结论:提出的重新排序方案有效地以平衡的梯度矩中的序列减少了涡流伪像。与以前的方法相反,我们利用采样信号动力学的周期性,可以使涡流引起的有效K空间采样和最小化伪像。

Purpose: To minimize eddy current artifacts in periodic pulse sequences with balanced gradient moments as, e.g., used for quantitative MRI. Theory and Methods: Eddy current artifacts in balanced sequences result from large jumps in k-space. In quantitative MRI, one often samples some spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps in temporal succession without changing the overall trajectory. This reordering can be formulated as a traveling salesman problem and we tackle the discrete optimization with a simulated annealing algorithm. Results: Compared to the default ordering, we observe a substantial reduction of artifacts in the reconstructed images and the derived quantitative parameter maps. Comparing two variants of our algorithm, one that resembles the pairing approach originally proposed by Bieri et al., and one that minimizes all k-space jumps equally, we observe slightly lower artifact levels in the latter. Conclusion: The proposed reordering scheme effectively reduces eddy current artifacts in sequences with balanced gradient moments. In contrast to previous approaches, we capitalize on the periodicity of the sampled signal dynamics, enabling both efficient k-space sampling and minimizing artifacts caused by eddy currents.

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