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

作     者：Shi, Gen Yin, Lin Bian, Zhongwei Chen, Ziwei An, Yu Hui, Hui Tian, Jie 

作者机构：Beihang Univ Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol China Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Natl Key Lab Kidney Dis Beijing 100853 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT》 (IEEE Trans. Instrum. Meas.)

年 卷 期：2025年第74卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 

基　　金：National Natural Science Foundation of China [62027901, 624B2018, 92359304, U23A6011, 81227901] Beijing Natural Science Foundation [JQ22023, L242064, L232097] National Key Research and Development Program of China [2023YFC3402800] Chinese Academy of Sciences (CAS) Youth Innovation Promotion Association [Y2022055] 

主　　题：Image denoising knowledge distillation mag- netic particle imaging (MPI) mag- netic particle imaging (MPI) real-time real-time Image denoising knowledge distillation mag- netic particle imaging (MPI) real-time 

摘      要：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://***/shigen-StoneRoot/***.