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

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

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.

Citation

McKay, M. J., Weber, K. A., Wesselink, E. O., Smith, Z. A., Abbott, R., Anderson, D. B., Ashton-James, C. E., Atyeo, J., Beach, A. J., Burns, J., Clarke, S., Collins, N. J., Coppieters, M. W., Cornwall, J., Crawford, R. J., De Martino, E., Dunn, A. G., Eyles, J. P., Feng, H. J., Fortin, M., …Elliott, J. M. (2024). MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. Journal of Imaging, 10(11), Article 262. https://doi.org/10.3390/jimaging10110262

• Journal Article Type: Review
• Acceptance Date: Oct 18, 2024
• Online Publication Date: Oct 22, 2024
• Publication Date: 2024-11
• Deposit Date: Oct 28, 2024
• Publicly Available Date: Oct 28, 2024
• Journal: Journal of Imaging
• Electronic ISSN: 2313-433X
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 11
• Article Number: 262
• DOI: https://doi.org/10.3390/jimaging10110262
• Keywords: artificial intelligence; neural networks; machine learning; MR imaging; muscle fat infiltration; public datasets; normative reference data