Last updated:
Author(s):
Rafael Garcia-Dias, Cristina Scarpazza, Lea Baecker, Sandra Vieira, Walter H.L. Pinaya, Aiden Corvin, Alberto Redolfi, Barnaby Nelson, Benedicto Crespo-Facorro, Colm McDonald, Diana Tordesillas-Gutiérrez, Dara Cannon, David Mothersill, Dennis Hernaus, Derek Morris, Esther Setien-Suero, Gary Donohoe, Giovanni Frisoni, Giulia Tronchin, João Sato, Machteld Marcelis, Matthew Kempton, Neeltje E.M. van Haren, Oliver Gruber, Patrick McGorry, Paul Amminger, Philip McGuire, Qiyong Gong, René S. Kahn, Rosa Ayesa-Arriola, Therese van Amelsvoort, Victor Ortiz-García de la Foz, Vince Calhoun, Wiepke Cahn, Andrea Mechelli
Publish date:
4 July 2020
Journal:
NeuroImage
PubMed ID:
32634595

Abstract

•We present Neuroharmony, a harmonization tool for images from unseen scanners.•We developed Neuroharmony using a total of 15,026 sMRI images.•The tool was able to reduce scanner-related bias from unseen scans.•Neuroharmony represents a significant step towards imaging-based clinical tools.•Neuroharmony is available at https://github.com/garciadias/Neuroharmony. We present Neuroharmony, a harmonization tool for images from unseen scanners. We developed Neuroharmony using a total of 15,026 sMRI images. The tool was able to reduce scanner-related bias from unseen scans. Neuroharmony represents a significant step towards imaging-based clinical tools. Neuroharmony is available at https://github.com/garciadias/Neuroharmony.

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Institution:
King's College London, Great Britain

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