Deep learning for improving the spatial resolution of magnetic particle imaging.

Journal: Physics in medicine and biology
Published Date: Jun 10, 2022

Abstract

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.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.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.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

Authors

  • Yaxin Shang
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100069, People's Republic of China.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Liwen Zhang
    National Engineering Laboratory of Big Data Analytics, Xi'an Jiaotong University, Xi'an 710049, China.
  • Xiangjun Wu
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Lin Yin
    Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.
  • Hui Hui
    Department of Emergency, The First Medical Center to Chinese People's Liberation Army General Hospital, Beijing, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

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