Magnetic Particle Imaging (MPI) enables noninvasive temperature imaging without depth limitations. However, due to the lack of effective calibration strategies that can simultaneously address issues such as calibration infeasibility and environmental mismatch, its practical in vivo application remains challenging. In this work, we propose a novel in vivo temperature imaging method based on a dual-mode magnetic particle spectroscopy/magnetic particle imaging (MPS/MPI) system. First, MPS is employed to capture the differences in harmonic phase responses of magnetic nanoparticles (MNPs) under in vivo and in vitro conditions, thereby enabling the construction of calibration functions that are consistent with the in vivo environment. Second, an MLP based calibration strategy is proposed, which accounts for non-ideal deviations from the approximately linear temperature-phase relationship and integrates multi-parameter information into a unified network, thereby enabling accurate and stable temperature mapping. Comprehensive simulation, in vitro, and in vivo experiments demonstrate that, compared with conventional phantom-based temperature mapping methods, the proposed method reduces the in vivo temperature reconstruction error by approximately 17.24% and achieves an average absolute temperature error below $1.257~^{\circ } $ C. These results verify the feasibility of accurate in vivo temperature imaging using MPI and provide essential technical support for temperature-sensitive applications, including magnetic hyperthermia.

Recently, Magnetic Particle Imaging, an emerging functional imaging modality, has exhibited outstanding spatial-temporal resolution and sensitivity. The general reconstruction pipeline of Magnetic Particle Imaging involves calibrating a System Matrix and then solving an ill-posed inverse problem combined with the measured particle signals. However, the introduction of noise during the System Matrix calibration procedure is inevitable, which degrades the detailed information in the reconstructed images. Therefore, frequency selection methods based on signal-to-noise ratio are commonly adopted. However, these methods lead to a decrease in the available high-frequency components, which damages the spatial resolution. To address this problem, we propose an unsupervised memory-guided denoising framework with unpaired noisy-clean System Matrix components, called U-N2C. Specifically, we design a Pattern Memory Block to memorize System Matrix patterns, directed by a position-aware frequency index embedding. Meanwhile, we devise a Noise Memory Block to implicitly approximate noise distributions. With the guidance of our dual memory blocks, we can disentangle the noise and content of the System Matrix in the latent space. Furthermore, benefiting from the ability to model complex noise, our method can generate pseudo but high-quality noisy-clean pairs and further enhance our denoising capability. Experiments on both synthetic and real noise demonstrate that our U-N2C achieves cutting-edge performance compared to other methods. Moreover, we conduct extensive qualitative and quantitative ablation studies to verify the effectiveness of our method. Our code has been available at U-N2C.