Electromagnetic (EM) radiofrequency (RF) safety testing of elongated active implantable medical devices (AIMD) during magnetic resonance imaging (MRI) requires an RF response model of the implant to assess a wide range of exposure conditions. The model must be validated using a sufficiently large set of incident tangential electric field (Etan) conditions that provide diversified exposure. Until now, this procedure was very time consuming and often resulted in poorly definedEtanconditions. In this paper, we propose a test field diversity (TFD) validation method that provides more diverse exposure conditions of high fidelity, thereby decreasing the number of implant routings to be tested. The TFD method is based on the finding that the amplitude and phase ofEtanalong a single lead path in a cylindrical phantom can be sufficiently varied by changing the polarization of the incident 64 and 128 MHz magnetic fields inside standard birdcage test coils. The method is validated, its benefits are demonstrated, and an uncertainty budget is developed. First, the numerically determined field conditions were experimentally verified. The RF transfer function of a 90 cm long spinal cord stimulator was successfully validated with the TFD approach and excitation conditions that cover a >10 dB dynamic range of RF-heating enhancement factors (for identical trajectory-averaged incident field strength). The new TFD method yields an improved and reliable validation of the AIMD RF response model with low uncertainty, i.e., <1.5 dB, for both 1.5 and 3.0 T evaluations.

中文翻译：

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一种新型的测试场分集方法，用于证明有源植入式医疗设备的磁共振成像安全性。

在磁共振成像（MRI）期间对细长的有源可植入医疗设备（AIMD）进行电磁（EM）射频（RF）安全性测试需要植入物的RF响应模型来评估各种暴露条件。必须使用足够多的入射切向电场（Etan）条件集对模型进行验证，该条件可提供多种暴露。到目前为止，此过程非常耗时，并且经常导致定义不明确的Etan条件。在本文中，我们提出了一种测试场分集（TFD）验证方法，该方法可提供更多种高保真度的曝光条件，从而减少要测试的植入物布线的数量。TFD方法基于以下发现：可以通过改变标准鸟笼测试线圈内入射的64和128 MHz磁场的极化来充分改变圆柱体模型中单引线路径的Etanalong的幅度和相位。验证了该方法，证明了其好处，并确定了不确定性预算。首先，通过实验验证了数值确定的现场条件。90厘米长的脊髓刺激器的RF传递函数已通过TFD方法和激发条件成功验证，该条件涵盖了RF加热增强因子的动态范围> 10 dB（对于相同的轨迹平均入射场强度）。新的TFD方法可对AIMD RF响应模型进行改进且可靠的验证，且不确定性低，即对于两个均小于1.5 dB。