Fat-water MRI separation using deep complex convolution network.

Journal: Magma (New York, N.Y.)
Published Date: Jul 3, 2025

Abstract

OBJECTIVE: Deep complex convolutional networks (DCCNs) utilize complex-valued convolutions and can process complex-valued MRI signals directly without splitting them into two real-valued magnitude and phase components. The performance of DCCN and real-valued U-Net is thoroughly investigated in the physics-informed subject-specific ad-hoc reconstruction method for fat-water separation and is compared against a widely used reference approach.

Authors

  • Moorthy Ganeshkumar
    Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
  • Devasenathipathy Kandasamy
    Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
  • Raju Sharma
    Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India.
  • Amit Mehndiratta

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