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首页> 外文期刊>Applied Magnetic Resonance >Optimization of an 8-Channel Loop-Array Coil for a 7 T MRI System with the Guidance of a Co-Simulation Approach
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Optimization of an 8-Channel Loop-Array Coil for a 7 T MRI System with the Guidance of a Co-Simulation Approach

机译:协同仿真方法指导下的7 T MRI系统8通道环形阵列线圈的优化

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摘要

Minimizing coupling between coil elements is technically challenging in designing large-sized, volume-type phased-array coils for human head imaging at ultrahigh fields, e.g., 7 T. As a widely used decoupling method, the capacitive decoupling method has shown excellent performance for loop array. However, building a multi-channel loop array with capacitive decoupling method is laborious that tuning frequency and matching of one coil element will affect adjacent elements and even next adjacent elements. In this study, we made an 8-channel looparray transmit/receive radio-frequency coil on a 7 T magnetic resonance imaging system with the guidance of frequency domain three-dimensional electromagnetic and radio-frequency circuit co-simulation. The position of decoupling capacitors was investigated and values of all capacitors were predicted from co-simulation. The co-simulation approach cost about 2 days and the error of the predicted and practical capacitance was 5 %. To demonstrate the accuracy of simulation, we evaluated the simulated and measured S-parameter matrixes and B_1~+ profiles in a birdcage-like excitation mode on a cylindrical water phantom. In addition, B_1_+ maps and images of human head were shown with the fabricated coil. To demonstrate the parallel imaging performance of this coil array, GRE images using GRAPPA acceleration with the reduction factor R of 1, 2, 3, and 4 were acquired.
机译:在设计用于超高磁场(例如7 T)的人头成像的大型,体积型相控阵线圈时,最小化线圈元件之间的耦合在技术上具有挑战性。作为一种广泛使用的去耦方法,电容性去耦方法已显示出优异的性能。循环数组。然而,用电容去耦方法构建多通道回路阵列是费力的,因为一个线圈元件的调谐频率和匹配将影响相邻元件甚至下一相邻元件。在这项研究中,我们在频域三维电磁和射频电路协同仿真的指导下,在7 T磁共振成像系统上制作了一个8通道环形阵列发射/接收射频线圈。研究了去耦电容器的位置,并通过协同仿真预测了所有电容器的值。协同仿真方法耗时约2天,预计电容和实际电容的误差为5%。为了证明仿真的准确性,我们在圆柱水模型上以鸟笼状激励模式评估了仿真和测量的S参数矩阵和B_1〜+分布。此外,用制作好的线圈显示了B_1_ +的人体头部图和图像。为了证明该线圈阵列的平行成像性能,使用GRAPPA加速获得了GRE图像,其还原因子R为1、2、3和4。

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