Content-Aware Distillation Network for Real-Time Magnetic Particle Imaging

Abstract

Magnetic particle imaging (MPI) has emerged as a promising medical imaging technique known for its high sensitivity and high imaging speed, making real-time, in vivo imaging feasible. However, existing MPI systems often require multiple repetition measurements for signal denoising. Few repetitions may result in low-quality images with increased noise, whereas many repetitions compromise temporal resolution and may introduce significant motion artifacts in dynamic imaging. Therefore, to fully exploit the advantages of MPI in real-time imaging, it is crucial to reduce the repetition number while maintaining high-quality images. In this study, we introduced a novel deep-learning (DL)-based approach, the content-aware distillation network (CAD-Net), for accelerated MPI. The method reconstructs high-quality images by denoising noisy images, typically acquired with a limited number of repetitions (tens of milliseconds). CAD-Net incorporates a proposed multiscale content-aware (MCA) block to accurately model noise distribution and enhance denoising performance. In addition, we proposed an activation-mask-based distillation strategy to reduce model processing time, particularly important for real-time imaging. Evaluation on a public real-world dataset, OpenMPI, and a simulation dataset, proved that CAD-Net outperformed existing methods in denoising performance and model efficiency. Compared to traditional methods based on multiple measurements, CAD-Net increased the frames per second (FPS) metric by approximately 70 times. Experiments on in-house data demonstrated the applicability of CAD-Net in MPI denoising in in vitro and in vivo imaging. CAD-Net improved image quality in real-time denoising with only a marginal increase in time cost. The code and data will be available at: https://github.com/shigen-StoneRoot/CAD-Net.git.