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，近年来受到日益广泛的关注。MPI的优势在于其高时间分辨率，且该技术不会使样本暴露于任何形式的电离辐射。其成像原理基于磁性纳米粒子对外加磁场的非线性响应。需要通过接收线圈测量到的电信号重建粒子浓度分布。由于重建问题的不适定性，学界已提出多种正则化重建方法，涵盖早期停止法、经典吉洪诺夫正则化、迭代方法乃至现代机器学习方法。本研究针对最后一类方法作出贡献：我们提出一种基于通用零样本去噪器并结合ℓ¹先验的即插即用方法。方法：我们在混合数据集上验证该方法的重建参数，并将其与基线吉洪诺夫方法、深度图像先验方法以及先前的PP-MPI方法（一种基于MPI友好数据训练去噪器的即插即用方法）进行比较。主要结果：我们在三维开放MPI数据集上对零样本即插即用方法进行了定量与定性评估。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：所提方法采用的零样本去噪器无需针对MPI任务进行专门训练，从而节省了训练成本。同时，该方法为未来MPI应用场景提供了一种潜在可行的技术路径。