Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Journal: Frontiers in bioscience (Landmark edition)
PMID: 35345331

Abstract

BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI.

Authors

  • Chad A Arledge
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Deeksha M Sankepalle
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • William N Crowe
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Lulu Wang
    c Center of Community Health Services, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang Province, China.
  • Dawen Zhao
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.

Keywords

External Resources

Popular Topics
Recent Journals