Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Journal: Magma (New York, N.Y.)
PMID: 39891798

Abstract

OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning.

Authors

  • S Sophie Schauman
    Department of Radiology, Stanford University, Stanford, CA, USA. sophie.schauman.academic@gmail.com.
  • Siddharth S Iyer
    Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.
  • Christopher M Sandino
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Mahmut Yurt
  • Xiaozhi Cao
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Congyu Liao
    Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.
  • Natthanan Ruengchaijatuporn
    Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand.
  • Itthi Chatnuntawech
    National Nanotechnology Center, Pathum Thani, Thailand.
  • Elizabeth Tong
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Kawin Setsompop
    Department of Radiology, Harvard Medical School, Boston, MA, USA.

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