Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a plug-and-play approach based on a generic zero-shot denoiser with an $\ell^1$-prior. Moreover, we develop parameter selection strategies. Finally, we quantitatively and qualitatively evaluate the proposed algorithmic scheme on the 3D Open MPI data set with different levels of preprocessing.

翻译：磁粒子成像（MPI）是一种新兴的医学成像模态，近年来受到越来越多的关注。MPI的优势包括高时间分辨率，且该技术不会使样本暴露于任何电离辐射。它基于磁性纳米粒子对施加磁场的非线性响应。根据接收线圈中测量的电信号，需要重建粒子浓度。由于重建问题的不适定性，研究者提出了多种正则化方法，从早期停止方法、经典Tikhonov正则化、迭代方法，到现代机器学习方法。本研究贡献于后者：我们提出了一种基于通用零样本去噪器与$\ell^1$先验的即插即用方法。此外，我们开发了参数选择策略。最后，我们在经过不同程度预处理的3D开放MPI数据集上对该算法方案进行了定量和定性评估。