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

Author:

Iglesias Juan E.123ORCID,Billot Benjamin2ORCID,Balbastre Yaël1ORCID,Magdamo Colin4ORCID,Arnold Steven E.4ORCID,Das Sudeshna4ORCID,Edlow Brian L.145ORCID,Alexander Daniel C.2ORCID,Golland Polina3,Fischl Bruce13ORCID

Affiliation:

1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

2. Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

3. Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA.

4. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

5. Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA.

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference88 articles.

1. FreeSurfer

2. FSL

3. W. D. Penny K. J. Friston J. T. Ashburner S. J. Kiebel T. E. Nichols Statistical Parametric Mapping: The Analysis of Functional Brain Images (Elsevier 2011).

4. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages

5. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods

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