Deep Learned Super Resolution of System Matrices for Magnetic Particle Imaging


Gungor A., Askin B., Soydan D. A. , Baris Top C., Cukur T.

43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 1 - 05 November 2021, pp.3749-3752 identifier identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/embc46164.2021.9630601
  • City: Virtual, Online
  • Country: Mexico
  • Page Numbers: pp.3749-3752

Abstract

© 2021 IEEE.Magnetic Particle Imaging (MPI) is a new imaging technique that allows high resolution & high frame-rate imaging of magnetic nanoparticles (MNP). It relies on the nonlinear response of MNPs under a magnetic field. The imaging process can be modeled linearly, and then image reconstruction can be case as an inverse problem using a measured system matrix (SM). However, this calibration measurement is time consuming so it reduces practicality. In this study, we proposed a novel method for accelerating the SM calibration based on joint super-resolution (SR) and denoising of sensitivty maps (i.e., rows of SM). The proposed method is based on a deep convolutional neural network (CNN) architecture with residual-dense blocks. Model training was performed using noisy SM measurements simulated for varying MNP size and gradient strengths. Comparisons were performed against conventional low-resolution SM calibration, noisy high-resolution SM calibration, and bicubic upsampling of low-resolution SM. We show that the proposed method improves high-resolution SM recovery, and in turn leads to improved resolution and quality in subsequently reconstructed MPI images.