Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

Journal: Stroke
PMID: 39145386

Abstract

BACKGROUND: Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images.

Authors

  • Yutong Chen
    Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Daniel Tozer
    Department of Clinical Neuroscience, Stroke Research Group, University of Cambridge, United Kingdom (Y.C., D.T., R.L., H.S.M.).
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Anil Tuladhar
    Department of Neurology, Radboud University Medical Center, Donders Center for Medical Neurosciences, Nijmegen, the Netherlands (H.L., A.T., F.E.D.L.).
  • Frank Erik De Leeuw
    Department of Neurology, Radboud University Medical Center, Donders Center for Medical Neurosciences, Nijmegen, the Netherlands (H.L., A.T., F.E.D.L.).
  • Hugh S Markus
    Department of Clinical Neuroscience, Stroke Research Group, University of Cambridge, United Kingdom (Y.C., D.T., R.L., H.S.M.).

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