In Physics in medicine and biology
OBJECTIVE : Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide (SPIO) 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.
Shang Yaxin, Liu Jie, Liu Yanjun, Zhang Bo, Wu Xiangjun, Zhang Liwen, Tong Wei, Hui Hui, Tian Jie
2023-Jan-23
Deep learning, Unidirectional Cartesian, magnetic particle imaging, point spread function, superparamagnetic iron oxide nanoparticles
