CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
PMID:

Abstract

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification.

Authors

  • Meera Srikrishna
    Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden.
  • Nicholas J Ashton
    Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
  • Alexis Moscoso
    Nuclear Medicine Department and Molecular Imaging Group, University Hospital CHUS-IDIS, Travesía da Choupana s/n, Santiago de Compostela 15706, Spain.
  • Joana B Pereira
    Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Rolf A Heckemann
    Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden.
  • Danielle van Westen
    Department of Clinical Sciences, Diagnostic Radiology, Lund University Sweden; Department of Imaging and Function, Skånes University Hospital, Lund, Sweden.
  • Giovanni Volpe
    Department of Physics, University of Gothenburg, Sweden.
  • Joel Simrén
    Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden.
  • Anna Zettergren
    Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden.
  • Silke Kern
    Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden.
  • Lars-Olof Wahlund
    Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Bibek Gyanwali
    Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Saima Hilal
    Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore.
  • Joyce Chong Ruifen
    Memory Aging and Cognition Centre, National University Health System, Singapore.
  • Henrik Zetterberg
    Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden.
  • Kaj Blennow
    Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden.
  • Eric Westman
    Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Christopher Chen
  • Ingmar Skoog
    Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden.
  • Michael Schöll
    Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden; Dementia Research Centre, Institute of Neurology, University College London, London, UK; Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden. Electronic address: michael.scholl@neuro.gu.se.