A Design Tool for Magnetic Resonance Imaging Gradient Coils Using DUCAS with Weighted Nodes and Initial Current Potentials
A Design Tool for Magnetic Resonance Imaging Gradient Coils Using DUCAS with Weighted Nodes and Initial Current Potentials
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DOI：
10.1109/tmag.2013.2275411
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发表时间：
2013-12-01
影响因子：
2.1
通讯作者：
Abe, Mitsushi
中科院分区：
文献类型：
--
作者：
Abe, Mitsushi
DUCAS是一种计算机代码，使用任意磁场分布作为输入，并产生片电流分布作为输出。该代码进行了改进，适用于磁共振成像（MRI）梯度线圈（GC）的设计。DUCAS计算电流电位（CP）分布，线圈导体沿着轮廓放置，这些轮廓是电流流动线。本文提出了一种改进的基于节点CP和输入初始CP权值的DUCAS算法。该算法通过对初始CP分布与奇异值分解（SVD）特征分布求和得到CP分布。权重修改特征分布。初始CP分布用于获得平滑的流线，然后获得线圈图案。改进的DUCAS适合避免复杂的电流分布并获得易于制造的线圈图案。首先假设初始CP分布，然后DUCAS用权重补偿给定磁场分布的CP。本文介绍了计算算法，并显示了GC设计与它获得。
DUCAS is a computer code using an arbitrary magnetic field distribution as input and producing a sheet current distribution as output. The code was improved for application to the magnetic resonance imaging (MRI) gradient coil (GC) design. DUCAS computes the current potential (CP) distribution, and the coil conductors are placed along the contours, which are current flow lines. An improved algorithm with weights on node CPs and input initial CPs is described for use with DUCAS. The algorithm obtains the CP distribution by summing singular value decomposition (SVD) eigendistributions with the initial CP distribution. The weights modify the eigendistributions. The initial CP distribution is used to get smooth flow lines and then coil patterns. The improved DUCAS is suitable for avoiding a complex current distribution and to obtain coil patterns that are easy to manufacture. The initial CP distribution is assumed first, then DUCAS compensates the CPs for the given magnetic field distribution with the weights. This paper describes the computational algorithm and shows a GC design obtained with it.