Evidence map›Paper›PMID 36724222›Full record

ArticleScience advances2023

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

Juan E Iglesias, Benjamin Billot, Yaël Balbastre, Colin Magdamo, Steven E Arnold, Sudeshna Das, Brian L Edlow, Daniel C Alexander, Polina Golland, Bruce Fischl

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers.

0numbers the graph read from it
0cells of the map it votes in
132citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

132 citing papers in PubMed.

  1. Article
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    Article
  4. [European journal of nuclear medicine and molecular imaging · 2026
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72 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Juan E IglesiasAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7569-173X
Benjamin BillotCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.ORCID 0000-0002-3018-1282
Yaël BalbastreAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8758-9978
Colin MagdamoDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8965-4630
Steven E ArnoldDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-9487-415X
Sudeshna DasDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-9486-6811
Brian L EdlowAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7235-8456
Daniel C AlexanderCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.ORCID 0000-0003-2439-350X
Polina GollandComputer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA.
Bruce FischlAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-2413-1115

Funding

A Connectome-Based Clinical Trial Platform to Promote Early Recovery of Consciousness after Traumatic ComaDP2HD101400 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI EDLOW, BRIAN L. · 2019 to 2019
$2.5M
NICHD NIH HHS DP2 HD101400
6 · The paper itself

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.

Indexed as

Artificial IntelligenceNeuroimagingBrainHumansMagnetic Resonance ImagingProspective Studies

Identifiers

PMID36724222
PMCPMC9891693

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.