Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging. (21st February 2023)

• Record Type: Journal Article
• Authors: Shang, Yaxin; Liu, Jie; Liu, Yanjun; Zhang, Bo; Wu, Xiangjun; Zhang, Liwen; Tong, Wei; Hui, Hui; Tian, Jie
• Is Part Of: Physics in medicine & biology. Volume 68:Number 4(2023)
• Publication Date: 2023-02-21
• Subjects: deep learning -- magnetic particle imaging -- point spread function -- superparamagnetic iron oxide nanoparticles -- unidirectional Cartesian; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acb584
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective . Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI. Methods . AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data. Results . The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods. Significance . The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.