MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

Marnee J. McKay*, Kenneth A. Weber, Evert O. Wesselink, Zachary A. Smith, Rebecca Abbott, David B. Anderson, Claire E. Ashton-James, John Atyeo, Aaron J. Beach, Joshua Burns, Stephen Clarke, Natalie J. Collins, Michel W. Coppieters, Jon Cornwall, Rebecca J. Crawford, Enrico De Martino, Adam G. Dunn, Jillian P. Eyles, Henry J. Feng, Maryse FortinMelinda M. Franettovich Smith, Graham Galloway, Ziba Gandomkar, Sarah Glastras, Luke A. Henderson, Julie A. Hides, Claire E. Hiller, Sarah N. Hilmer, Mark A. Hoggarth, Brian Kim, Navneet Lal, Laura LaPorta, John S. Magnussen, Sarah Maloney, Lyn March, Andrea G. Nackley, Shaun P. O’Leary, Anneli Peolsson, Zuzana Perraton, Annelies L. Pool-Goudzwaard, Margaret Schnitzler, Amee L. Seitz, Adam I. Semciw, Philip W. Sheard, Andrew C. Smith, Suzanne J. Snodgrass, Justin Sullivan, Vienna Tran, Stephanie Valentin, David M. Walton, Laurelie R. Wishart, James M. Elliott

*Corresponding author for this work

Research output: Contribution to JournalReview articleAcademicpeer-review

Abstract

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is ‘typical’ age-related muscle composition is essential to accurately identify and evaluate what is ‘atypical’. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field.

Original languageEnglish
Article number262
Pages (from-to)1-17
Number of pages17
JournalJournal of Imaging
Volume10
Issue number11
Early online date22 Oct 2024
DOIs
Publication statusPublished - Nov 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • artificial intelligence
  • machine learning
  • MR imaging
  • muscle fat infiltration
  • neural networks
  • normative reference data
  • public datasets

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