Deep learning for improving the spatial resolution of magnetic particle imaging. (21st June 2022)

• Record Type: Journal Article
• Authors: Shang, Yaxin; Liu, Jie; Zhang, Liwen; Wu, Xiangjun; Zhang, Peng; Yin, Lin; Hui, Hui; Tian, Jie
• Is Part Of: Physics in medicine & biology. Volume 67:Number 12(2022)
• Publication Date: 2022-06-21
• Subjects: deep learning -- magnetic particle imaging -- spatial resolution -- superparamagnetic iron oxide nanoparticles; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac6e24
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective. Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast. Approach. Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings. Main results. We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two. Significance. This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.