Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling.

Journal: PloS one
Published Date: Feb 21, 2020

Abstract

PURPOSE: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.

Authors

  • Seyed Amir Hossein Hosseini
    Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States of America.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sebastian Weingärtner
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
  • Steen Moeller
    Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
  • Matthias Stuber
    Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
  • Kâmil Uğurbil
    Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
  • Mehmet Akçakaya
    Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota.

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