Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.

Journal: Magnetic resonance in medicine
PMID:

Abstract

PURPOSE: To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI.

Authors

  • Munendra Singh
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Sultan Z Mahmud
    Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Vivek Yedavalli
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Jinyuan Zhou
    Cancer Hospital Affiliated to Harbin Medical University, Heilongjiang, Harbin 150000, China.
  • David Olayinka Kamson
    Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA.
  • Peter van Zijl
    Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
  • 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.