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Joint design of excitation k-space trajectory and RF pulse for small-tip 3D tailored excitation in MRI

机译:MRI小尖端3D量身定制的激发k空间轨迹和RF脉冲的联合设计

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摘要

We propose a new method for the joint design of k-space trajectory and RF pulse in 3D small-tip tailored excitation. Designing time-varying RF and gradient waveforms for a desired 3D target excitation pattern in MRI poses a non-linear, non-convex, constrained optimization problem with relatively large problem size that is difficult to solve directly. Existing joint pulse design approaches are therefore typically restricted to predefined trajectory types such as EPI or stack-of-spirals that intrinsically satisfy the gradient maximum and slew rate constraints and reduce the problem size (dimensionality) dramatically, but lead to suboptimal excitation accuracy for a given pulse duration. Here we use a 2nd-order B-spline basis that can be fitted to an arbitrary k-space trajectory, and allows the gradient constraints to be implemented efficiently. We show that this allows the joint optimization problem to be solved with quite general k-space trajectories. Starting from an arbitrary initial trajectory, we first approximate the trajectory using B-spline basis, and then optimize the corresponding coefficients. We evaluate our method in simulation using four different k-space initializations: stack-of-spirals, SPINS, KT-points, and a new method based on KT-points. In all cases, our approach leads to substantial improvement in excitation accuracy for a given pulse duration. We also validated our method for inner-volume excitation using phantom experiments. The computation is fast enough for online applications.
机译:我们提出了一种在3D小尖端定制激励中联合设计k空间轨迹和RF脉冲的新方法。为MRI中所需的3D目标激励模式设计时变的RF和梯度波形会带来非线性,非凸,受约束的优化问题,且问题规模较大,难以直接解决。因此,现有的联合脉冲设计方法通常仅限于预定义的轨迹类型,例如EPI或螺旋堆栈,它们本质上满足梯度最大值和摆率约束,并显着减小了问题的大小(维数),但导致激励精度不理想。给定脉冲持续时间。在这里,我们使用可以适合任意k空间轨迹的二阶B样条曲线基础,并且可以有效地实现梯度约束。我们表明,这使联合优化问题可以通过相当通用的k空间轨迹来解决。从任意初始轨迹开始,我们首先使用B样条曲线对轨迹进行近似,然后优化相应的系数。我们使用四种不同的k空间初始化对我们的仿真方法进行评估:螺旋堆栈,SPINS,KT点以及基于KT点的新方法。在所有情况下,我们的方法都可以在给定的脉冲持续时间内显着提高激励精度。我们还使用幻像实验验证了我们的内部体积激励方法。对于在线应用程序而言,计算速度足够快。

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