Optimized Data Acquisition for Image Reconstruction in Magnetic Particle Imaging (MPI) Based on Compressed Sensing

利用磁粒子成像(MPI)技术，可以实时定量地显示局部浓度的磁性纳米颗粒，具有高灵敏度和很好的空间分辨率。其基本思想是利用粒子的非线性磁化特性作为示踪剂。为此，该技术使用两个磁场，一方面是静态选择场，另一方面是动态交变场。一旦纳米粒子被带入交变磁场，它们就会产生一种非线性磁化强度，可以使用接收线圈进行测量。除了交变磁场的基频外，被测信号中还含有谐波，即基频的倍数振荡，这是由非线性引起的。在从施加的基波信号中分离出谐波后，可以确定纳米颗粒的浓度。使用静态选择字段来实现空间编码。在最初的实验研究中，MPI已经显示出比其他成像方式的优势。然而，在空间分辨率、信噪比和捕获时间方面，它还没有充分发挥其潜力。可以预见，信号处理和采样理论的最新进展，特别是在压缩感知(CS)领域的进展，将有助于图像质量和速度的提高。与最先进的科学相比，稀疏编码和压缩传感已经大大改进了其他成像方式，如磁共振成像(MRI)。到目前为止，标准小波变换主要作为合适的变换应用。然而，越来越多地寻求适合各自模式的信号特征的变换。这一战略也应在该项目中实施。基于待开发的模拟链的数据，以及医学工程研究所对不同MPI扫描仪拓扑的实现，可以利用信号处理研究所的专业知识，针对稀疏编码优化适配变换。

Using Magnetic Particle Imaging (MPI) a local concentration of magnetic nanoparticles can be displayed quantitatively in real-time with high sensitivity and with very good spatial resolution. The basic idea is to utilize the non-linear magnetization characteristics of the particles which are used as tracers. For this purpose, the technique employs two magnetic fields, on the one hand a static selection field, on the other hand a dynamic alternating field. Once the nanoparticles are brought into the alternating field they produce a non-linear magnetization, which can be measured using a receive coil. Apart from the fundamental frequency of the alternating field the measured signal also contains harmonics, i.e. oscillations with multiples of the fundamental frequency, which is caused by the non-linearity. After separation of the harmonics from the applied basic signal the concentration of the nanoparticles can be determined. Spatial encoding is achieved using the static selection field. In first experimental studies MPI has already shown advantages over other imaging modalities. However, it has not yet reached its full potential with respect to spatial resolution, signal-to-noise ratio and acquisition times. It can be expected that recent advancements in signal processing and sampling theory, especially in the field of compressed sensing (CS), will contribute to the enhancement of image quality and speed. Sparse coding and compressed sensing have already improved other imaging modalities like e.g. Magnetic Resonance Imaging (MRI) considerably compared to the state-of-the-art of science. So far, the standard wavelet transformations have predominantly been applied as suitable transformations. Increasingly though transformations are sought that suit the signal characteristics of the respective modality. This strategy shall also be pursued in this project. Based on data of a simulation chain, which is to be developed, and realizations of different MPI scanner topologies by the Institute of Medical Engineering adapted transformations can be optimized for sparse coding, capitalizing on the expertise of the Institute for Signal Processing.