Magnetic Particle Imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images. In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem. Its performance is numerically evaluated against a classical FISTA approach on simulated and real MPI data. We also compare the results to the state-of-the-art MPI reconstruction methods. In all cases, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.

中文翻译：

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一种用于磁粒子成像图像重建的基于小波的稀疏行动作方法

磁性粒子成像 (MPI) 是一种临床前成像技术，能够可视化磁性纳米粒子的时空分布。这种快速动态过程的图像重建依赖于有效解决不适定逆问题。当前从其测量中重建示踪剂浓度的方法要么适应 MPI 的图像特征，但具有较高的计算复杂性和较慢的收敛性，要么速度快但缺乏重建图像的图像质量。在这项工作中，我们提出了一种新的 MPI 重建方法，将两种方法的优点结合到一个算法中。底层稀疏先验基于未抽取小波变换，并集成到快速行动作框架中以解决相应的 MPI 最小化问题。它的性能是根据模拟和真实 MPI 数据的经典 FISTA 方法进行数值评估的。我们还将结果与最先进的 MPI 重建方法进行了比较。在所有情况下，我们的方法都显示出更好的重建结果，同时加快了底层行动作算法的收敛速度。