Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

Journal: Magnetic resonance in medicine
Published Date: Jun 15, 2023

Abstract

PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects.

Authors

  • Munendra Singh
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Shanshan Jiang
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Yuguo Li
    The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Peter van Zijl
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Jinyuan Zhou
    Cancer Hospital Affiliated to Harbin Medical University, Heilongjiang, Harbin 150000, China.
  • Hye-Young Heo
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. Electronic address: hheo1@jhmi.edu.

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