Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.

Journal: Magnetic resonance in medicine
Published Date: Feb 8, 2021

Abstract

PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging.

Authors

  • Yasheng Chen
    Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri.
  • Chunwei Ying
    Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
  • Michael M Binkley
    Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri.
  • Meher R Juttukonda
    Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Shaney Flores
    Washington University in St. Louis, St. Louis, Missouri, USA.
  • Richard Laforest
    Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Tammie L S Benzinger
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Hongyu An
    Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri.

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