Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.

Journal: International journal of computer assisted radiology and surgery
PMID: 39832070

Abstract

PURPOSE: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).

Authors

  • Yi-Kuan Liu
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Jorge Cisneros
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Girish Nair
    Division of Pulmonary and Critical Care, Beaumont Health, Royal Oak, MI, USA.
  • Craig Stevens
    Division of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA.
  • Richard Castillo
    Emory University, Department of Radiation Oncology, Atlanta, United States.
  • Yevgeniy Vinogradskiy
    Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
  • Edward Castillo
    Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States.

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