Research of magnetic particle imaging reconstruction based on the elastic net regularization. (August 2021)

• Record Type: Journal Article
• Authors: Chen, Xiaojun; Jiang, Zhenqi; Han, Xiao; Wang, Xiaolin; Tang, Xiaoying
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Magnetic particle imaging -- Regularization -- LASSO -- Elastic net; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102823
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The elastic net (EN) regularization is first introduced for MPI reconstruction. The reconstruction results based on the proposed method have nearly no artifacts. The time resolution is significantly improved. Four indicators are used for comparison of the results based on different models. Abstract: Magnetic particle imaging (MPI) is an emerging medical imaging modality that is based on the non-linear response of magnetic nanoparticles. The reconstruction task is an inverse problem and ill-posed in nature. The reconstruction results based on the state-of-the-art regularization model have many artifacts, and the time resolution should be improved significantly for real-time imaging. To this end, we first propose to use the elastic net (EN) regularization for MPI reconstruction. To obtain a good result with a short reconstruction time, we use the truncated system matrix and the truncated measurement for reconstruction research. We study the reconstruction quality by varying the threshold values and regularization parameters. We compare the reconstruction performance of the proposed model with the Tikhonov model and the least absolute shrinkage and selection operator (LASSO) model in terms of visualization and performance indicators. The MPI reconstruction results based on the EN have largely no artifacts, and the time resolution is approximately 10 times that of the LASSO model and 20 times that of the Tikhonov model. The conducted study demonstrated that the proposed method yields a significantly higher reconstruction quality and a higher time resolution than the state-of-the-art reconstruction methods based on the Tikhonov and LASSO models.