SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.

Journal: Science advances
Published Date: Feb 1, 2023

Abstract

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.

Authors

  • Juan E Iglesias
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Benjamin Billot
    Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
  • Yaël Balbastre
  • Colin Magdamo
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Steven E Arnold
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Sudeshna Das
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Brian L Edlow
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, USA.
  • Daniel C Alexander
    Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
  • Polina Golland
    CSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Bruce Fischl
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, United States; Department of Radiology, Harvard Medical School, United States; Division of Health Sciences and Technology and Engineering and Computer Science MIT, Cambridge, MA, United States.

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