A Green's function approach to local rf heating in interventional MRI.
A Green's function approach to local rf heating in interventional MRI.
复制标题
介入 MRI 中局部射频加热的格林函数方法。
DOI：
10.1118/1.1367860
复制
发表时间：
2001
期刊：
影响因子：
3.8
通讯作者：
Atalar,E
中科院分区：
文献类型：
--
作者：
Yeung,CJ;Atalar,E
目前针对外部射频线圈制定的局部射频 (rf) 加热安全法规可能不适用于介入 MRI 中越来越多地使用的内部射频线圈。这项工作提出了介入 MRI 设置中射频加热的两步模型：（1）射频脉冲样本中功率的空间分布（麦克斯韦方程组）； (2) 根据热传导和组织灌注（组织生物热方程）将该功率转换为温度变化。在局部射频加热的情况下，组织生物热方程被近似为线性、平移不变系统，并且完全由其格林函数表征。预期温度分布是通过将发射线圈比吸收率 (SAR) 分布与格林函数进行卷积（平均）来计算的。当输入 SAR 分布在空间中变化相对缓慢时，如外部射频线圈激励的情况，平均方法的选择对通过温度变化测量的预期加热几乎没有影响。但是，对于高度局部化的 SAR 分布，例如介入 MRI 中内部线圈遇到的分布，格林函数方法预测的加热与当前法规中的平均方法显着不同。我们认为，格林函数方法是更好的预测器，因为它基于生理模型。格林函数还得出 SAR 和 SAR 之间的时间常数和比例因子，它们都是组织灌注速率的函数。这强调了加热模型中灌注的至关重要性。该模型中所做的假设仅适用于局部射频加热，不适用于全身加热。
Current safety regulations for local radiofrequency (rf) heating, developed for externally positioned rf coils, may not be suitable for internal rf coils that are being increasingly used in interventional MRI. This work presents a two‐step model for rf heating in an interventional MRI setting: (1) the spatial distribution of power in the sample from the rf pulse (Maxwell's equations); and (2) the transformation of that power to temperature change according to thermal conduction and tissue perfusion (tissue bioheat equation). The tissue bioheat equation is approximated as a linear, shift‐invariant system in the case of local rf heating and is fully characterized by its Green's function. Expected temperature distributions are calculated by convolving (averaging) transmit coil specific absorption rate (SAR) distributions with the Green's function. When the input SAR distribution is relatively slowly varying in space, as is the case with excitation by external rf coils, the choice of averaging methods makes virtually no difference on the expected heating as measured by temperature change However, for highly localized SAR distributions, such as those encountered with internal coils in interventional MRI, the Green's function method predicts heating that is significantly different from the averaging method in current regulations. In our opinion, the Green's function method is a better predictor since it is based on a physiological model. The Green's function also elicits a time constant and scaling factor between SAR and that are both functions of the tissue perfusion rate. This emphasizes the critical importance of perfusion in the heating model. The assumptions made in this model are only valid for local rf heating and should not be applied to whole body heating.