| Title: | Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners |
| Journal: | NeuroImage |
| Published: | 4 Jul 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32634595/ |
| DOI: | https://doi.org/10.1016/j.neuroimage.2020.117127 |
| Citations: | 59 (23 in last 2 years) as of 8 Aug 2024 |
Publication 9124
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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.</p>
35 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 40323 | Using deep learning technology to make individualised inferences in brain-based disorders |
Enabling scientific discoveries that improve human health