Predicting MRI RF Exposure for Complex-shaped Medical Implants Using Artificial Neural Network
Predicting MRI RF Exposure for Complex-shaped Medical Implants Using Artificial Neural Network
复制标题
使用人工神经网络预测复杂形状医疗植入物的 MRI 射频暴露
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Ji Chen
中科院分区：
文献类型：
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作者：
Qianlong Lan;Jianfeng Zheng;Ji Chen
提出了一种基于复杂形状描述子的三层前馈人工神经网络快速预测方法，用于估算磁共振成像(MRI)下复杂形状植入器的射频暴露量。人工神经网络的输入由20个参数组成，这些参数基于复杂形状设备的尺寸和表面特征，输出是设备的峰值MRI RF暴露，以1克平均比吸收率(SAR1g)为单位。该方法在408个复杂形状的创伤植入器上得到了实现和验证。利用基于时域有限差分(FDTD)方法的全波电磁求解器，利用这些精确的三维CAD模型对器件的峰值SAR1g进行了数值计算。在408个设备中，326个用于训练神经网络，82个用于检验ANN预测的SAR1g值的有效性。结果表明，估计值与目标值的相关系数大于0.993，均方根误差小于6.21W/kg。
In this paper, a fast prediction method using a 3-layer feed-forward artificial neural network (ANN) based on complex shape descriptors was proposed to estimate the radio-frequency (RF) exposure for complex-shaped implantable devices under magnetic resonance imaging (MRI). The inputs of the ANN consist of 20 parameters based on dimensions and surface-based features of the complex-shaped devices, and the output is the peak MRI RF exposure, in terms of 1-gram averaged specific absorption rate (SAR1g), for the device. This method is implemented and validated with 408 complex-shaped trauma implantable devices. These accurate 3D CAD models are used to numerically calculate the peak SAR1g of the devices by using a full-wave electromagnetic solver based on finite-difference time-domain (FDTD) method. Among the 408 devices, 326 were used to train the neural network, while 82 were utilized to examine the validity of the SAR1g values predicted by the ANN. The results have shown that the correlation between the estimation and the target values was larger than 0.993, and the root mean square error was less than 6.21 W/kg.