Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

Abstract

Magnetic particle imaging (MPI) is an innovative imaging modality offering high spatio-temporal resolution for reconstructing magnetic particle distributions. To achieve high-quality MPI images, traditional methods such as the X-space method and system matrix (SM) method operate in the time and frequency domains, respectively. Both time and frequency domain signals essentially consist of harmonic components, meaning that the quality of the reconstruction is closely tied to the accuracy of these harmonic elements. However, the presence of background noise and the complexities associated with SM collection significantly hinder harmonic quality, leading to degradation of reconstruction fidelity. To tackle these challenges, we propose a unified framework based on harmonic knowledge to enhance the quality of reconstruction. In particular, our approach involves pretraining autoencoders by restoring masked SMs, thereby modeling the relationship between harmonics. Subsequently, using appropriate decoders, the pretrained encoder can be transferred to tasks such as spectrum denoising and SM super-resolution. Our framework's effectiveness is validated on these two tasks through simulation and publicly available datasets, where it consistently outperforms state-of-the-art (SOTA) methods. In addition, our framework shows marked improvements in reconstruction quality for both time and frequency domain data collected with our in-house MPI system.