DDMPI: Diffusion Denoising for Magnetic Particle Imaging at the Low Concentration

Magnetic particle imaging (MPI) has demonstrated its advantages of high sensitivity and temporal resolution in various preclinical applications. However, during the imaging process, the signal is susceptible to different noises, resulting in severe stripe artifacts in reconstructed MPI images. This phenomenon will be further aggravated in scenarios with low-concentration particles, which is a standard practice in biological applications, thereby seriously hindering the identification of key information. To solve this problem, we propose a joint optimization approach called diffusion denoising model for MPI (DDMPI) that integrates diffusion model with Transformer to remove the artifacts directly from MPI images obtained in the low-concentration scenarios. In DDMPI, a latent encoder generates prior features containing the relevance mapping between the contents and the artifacts within MPI images, and a conditional latent diffusion model optimizes these prior features. A U-shape Transformer module incorporates the prior features by a hierarchical integration module and utilizes a strip-self-attention module to capture the spatial distribution of the artifacts. Ablation experiments demonstrate the effectiveness of these modules in DDMPI. Extensive experiments, including simulation, phantom and in vivo experiments, demonstrate that DDMPI effectively removes artifacts and recovers fine details. Additionally, DDMPI is independent of the primary image reconstruction methods of various scanning devices. Thus, DDMPI can be not only practically applied to in vivo imaging but also flexibly combined with various existing MPI devices to effectively improve the imaging quality and provide critical information about diseases.