Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany.
Sensors (Basel). 2022 Jan 24;22(3):894. doi: 10.3390/s22030894.
Imaging of magnetic nanoparticles (MNPs) is of great interest in the medical sciences. By using resonant magnetoelectric sensors, higher harmonic excitations of MNPs can be measured and mapped in space. The proper reconstruction of particle distribution via solving the inverse problem is paramount for any imaging technique. For this, the forward model needs to be modeled accurately. However, depending on the state of the magnetoelectric sensors, the projection axis for the magnetic field may vary and may not be known accurately beforehand. As a result, the projection axis used in the model may be inaccurate, which can result in inaccurate reconstructions and artifact formation. Here, we show an approach for mapping MNPs that includes sources of uncertainty to both select the correct particle distribution and the correct model simultaneously.
医学科学对磁性纳米粒子（MNPs）的成像非常感兴趣。通过使用共振磁电传感器，可以测量和绘制 MNPs 的高次谐波激发，并在空间中进行绘制。通过求解反问题对粒子分布进行适当的重建对于任何成像技术都是至关重要的。为此，需要精确地建模正向模型。然而，根据磁电传感器的状态，磁场的投影轴可能会发生变化，并且可能无法事先准确地知道。因此，模型中使用的投影轴可能不准确，这可能会导致重建不准确和伪影形成。在这里，我们展示了一种映射 MNPs 的方法，该方法同时包含了对选择正确的粒子分布和正确的模型的不确定性源。
