Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China.
Precision Medicine Equipment Technology Reacher Center, Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.
Med Phys. 2024 Oct;51(10):7180-7190. doi: 10.1002/mp.17301. Epub 2024 Jul 14.
Magnetoencephalography (MEG) and magnetic resonance imaging (MRI) are non-invasive imaging techniques that offer effective means for disease diagnosis. A more straightforward and optimized method is presented for designing gradient coils which are pivotal parts of the above imaging systems.
A novel design method based on stream function combining an optimization algorithm is proposed to obtain highly linear gradient coil.
Two-dimensional Fourier expansion of the current field on the surface where the coil is located and the equipotential line of the expansion term superposition according to the number of turns of the coil are used to represent the coil shape. Particle swarm optimization is utilized to optimize the coil shape while linearity and field uniformity are used as parameters to evaluate the coil performance. Through this method, the main parameters such as input current distribution region, coil turns, desired magnetic field strength, expansion order and iteration times can be combined in a given solution space to optimize coil design.
Simulation results show that the maximum linearity spatial deviation of the designed bi-planar x-gradient coil compared with that of target field method is reduced from 14% to 0.54%, and that of the bi-planar z-gradient coil is reduced from 8.98% to 0.52%. Similarly, that of the cylindrical x-gradient coil is reduced from 2% to 0.1%, and that of the cylindrical z-gradient coil is reduced from 0.87% to 0.45%. The similar results are found in the index of inhomogeneity error. Moreover, it has also been verified experimentally that the result of measured magnetic field is consist with simulated result.
The proposed method provides a straightforward way that simplifies the design process and improves the linearity of designed gradient coil, which could be beneficial to realize better magnetic field in engineering applications.
脑磁图（MEG）和磁共振成像（MRI）是两种非侵入性成像技术，为疾病诊断提供了有效的手段。本文提出了一种更直接、更优化的梯度线圈设计方法，梯度线圈是上述成像系统的关键部分。
提出了一种基于流函数的新型设计方法，并结合优化算法，获得高度线性的梯度线圈。
根据线圈匝数，对线圈所在表面的电流场和扩展项等位面进行二维傅里叶展开，用展开项的叠加来表示线圈形状。利用粒子群算法优化线圈形状，同时将线性度和场均匀度作为评价线圈性能的参数。通过这种方法，可以将输入电流分布区域、线圈匝数、所需磁场强度、展开阶数和迭代次数等主要参数结合在给定的解空间中，实现线圈设计的优化。
仿真结果表明，与目标场方法相比，设计的双平面 x 梯度线圈的最大线性空间偏差从 14%降低到 0.54%，双平面 z 梯度线圈的最大线性空间偏差从 8.98%降低到 0.52%。同样，圆柱形 x 梯度线圈的最大线性空间偏差从 2%降低到 0.1%，圆柱形 z 梯度线圈的最大线性空间偏差从 0.87%降低到 0.45%。在不均匀性误差指数中也得到了类似的结果。此外，实验也验证了测量磁场的结果与模拟结果一致。
该方法为梯度线圈的设计提供了一种简洁的方法，简化了设计过程，提高了设计的线性度，这有利于在工程应用中实现更好的磁场。
