Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

• Published in: Physics in medicine & biology Vol. 68; no. 4; pp. 45014 - 45027
• Main Authors: Shang, Yaxin, Liu, Jie, Liu, Yanjun, Zhang, Bo, Wu, Xiangjun, Zhang, Liwen, Tong, Wei, Hui, Hui, Tian, Jie
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 21.02.2023
• Subjects: Anisotropy; deep learning; Diffusion Magnetic Resonance Imaging; Magnetic Iron Oxide Nanoparticles; magnetic particle imaging; Magnetic Phenomena; Magnetic Resonance Imaging; Magnetite Nanoparticles; point spread function; superparamagnetic iron oxide nanoparticles; unidirectional Cartesian; deep learning; unidirectional Cartesian; superparamagnetic iron oxide nanoparticles; magnetic particle imaging; point spread function
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/1361-6560/acb584

. 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. . 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. . 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. . The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.