DEQ-MPI: A Deep Equilibrium Reconstruction With Learned Consistency for Magnetic Particle Imaging.

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  • Author(s): Gungor A; Askin B; Soydan DA; Top CB; Saritas EU; Cukur T
  • Source:
    IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2024 Jan; Vol. 43 (1), pp. 321-334. Date of Electronic Publication: 2024 Jan 02.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 8310780 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-254X (Electronic) Linking ISSN: 02780062 NLM ISO Abbreviation: IEEE Trans Med Imaging Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, c1982-
    • Subject Terms:
      Diagnostic Imaging* ; Nanoparticles*; Neural Networks, Computer ; Magnetics ; Magnetic Phenomena ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods
    • Abstract:
      Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that is used to reconstruct data from subsequent scans. The ill-posed reconstruction problem can be solved by simultaneously enforcing data consistency based on the SM and regularizing the solution based on an image prior. Traditional hand-crafted priors cannot capture the complex attributes of MPI images, whereas recent MPI methods based on learned priors can suffer from extensive inference times or limited generalization performance. Here, we introduce a novel physics-driven method for MPI reconstruction based on a deep equilibrium model with learned data consistency (DEQ-MPI). DEQ-MPI reconstructs images by augmenting neural networks into an iterative optimization, as inspired by unrolling methods in deep learning. Yet, conventional unrolling methods are computationally restricted to few iterations resulting in non-convergent solutions, and they use hand-crafted consistency measures that can yield suboptimal capture of the data distribution. DEQ-MPI instead trains an implicit mapping to maximize the quality of a convergent solution, and it incorporates a learned consistency measure to better account for the data distribution. Demonstrations on simulated and experimental data indicate that DEQ-MPI achieves superior image quality and competitive inference time to state-of-the-art MPI reconstruction methods.
    • Publication Date:
      Date Created: 20230801 Date Completed: 20240103 Latest Revision: 20240103
    • Publication Date:
      20240103
    • Accession Number:
      10.1109/TMI.2023.3300704
    • Accession Number:
      37527298