Objective: 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. Approach: We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, DIP and the previous PP-MPI, which is a plug-and-play method with denoiser trained on MPI-friendly data. Main results: We offer a quantitative and qualitative evaluation of the zero-shot plug-and-play approach on the 3D Open MPI dataset. Moreover, we show the quality of the approach with different levels of preprocessing of the data. Significance: The proposed method employs a zero-shot denoiser which has not been trained for the MPI task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.

翻译：目的：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的优势包括其高时间分辨率，以及该技术不会使样本暴露于任何形式的电离辐射。它基于磁性纳米粒子对外加磁场的非线性响应。从接收线圈中测量的电信号中，必须重建出粒子浓度。由于重建问题的病态性，人们提出了各种正则化方法用于重建，范围从早期停止方法、经典的Tikhonov正则化和迭代方法，到现代的机器学习方法。在本工作中，我们为后一类方法做出贡献：我们提出了一种基于通用零样本去噪器并带有$\ell^1$先验的即插即用方法。方法：我们在一个混合数据集上验证了该方法的重建参数，并将其与基线Tikhonov、DIP以及先前的PP-MPI（一种使用在MPI友好数据上训练的去噪器的即插即用方法）进行了比较。主要结果：我们在3D Open MPI数据集上对零样本即插即用方法进行了定量和定性评估。此外，我们展示了该方法在不同数据预处理水平下的重建质量。意义：所提出的方法采用了一个未针对MPI任务进行训练的零样本去噪器，因此节省了训练成本。此外，它提供了一种未来可能应用于其他MPI场景的方法。