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

Authors: McKay MJWeber KAWesselink EOSmith ZAAbbott RAnderson DBAshton-James CEAtyeo JBeach AJBurns JClarke SCollins NJCoppieters MWCornwall JCrawford RJDe Martino EDunn AGEyles JPFeng HJFortin MFranettovich Smith MMGalloway GGandomkar ZGlastras SHenderson LAHides JAHiller CEHilmer SNHoggarth MAKim BLal NLaPorta LMagnussen JSMaloney SMarch LNackley AGO'Leary SPPeolsson APerraton ZPool-Goudzwaard ALSchnitzler MSeitz ALSemciw AISheard PWSmith ACSnodgrass SJSullivan JTran VValentin SWalton DMWishart LRElliott JM


Affiliations

1 Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
2 Division of Pain Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA.
3 Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences-Program Musculoskeletal Health, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands.
4 Department of Rehabilitation Medicine, University of Oklahoma, Norman, OK 73019, USA.
5 Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
6 Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia.
7 Disability Prevention Program, Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
8 School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, 4072 QLD, Australia.
9 School of Health Sciences and Social Work, Griffith University, Brisbane, QLD 4111, Australia.
10 Otago Medical School, University of Otago, Dunedin 9016, New Zealand.
11 Faculty of Health Sciences, Curtin University, Perth, WA 6845, Australia.
12 Department of Health Science and Technology, Aalborg University, Gistrup, 9260 North Jutland, Denmark.
13 Northern Sydney Local Health District, The Kolling Institute, St Leonards, NSW 2065, Australia.
14 Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC H4B 1R6, Canada.
15 Herston Imaging Research Facility, University of Queensland, Brisbane, QLD 4072, Australia.
16 Department of Physical Therapy, North Central College, Naperville, IL 60540, USA.
17 School of Rehabilitative and Health Sciences, Regis University, Denver, CO 80221, USA.
18 Center for Translational Pain Medicine, Department of Anesthesiology, School of Medicine, Duke University, Durham, NC 27710, USA.
19 Occupational and Environmental Medicine Centre, Department of Health Medicine and Caring Sciences, Unit of Clinical Medicine, Linköping University, 58183 Linköping, Sweden.
20 Department of Health Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, 58183 Linköping, Sweden.
21 School of Allied Health, La Trobe University, Melbourne, VIC 3086, Australia.
22 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
23 School of Medicine, University of Colorado, Aurora, CO 80045, USA.
24 Discipline of Physiotherapy, University of Newcastle, Callaghan, NSW 2308, Australia.
25 Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia.
26 School of Health & Social Care, Edinburgh Napier University, Edinburgh, Scotland EH11 4BN, UK.
27 School of Physical Therapy, Western University, London, ON N6A 3K7, Canada.
28 School of Medicine and Dentistry, Griffith University, Brisbane, QLD 4111, Australia.

Description

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.


Keywords: MR imagingartificial intelligencemachine learningmuscle fat infiltrationneural networksnormative reference datapublic datasets


Links

PubMed: https://pubmed.ncbi.nlm.nih.gov/39590726/

DOI: 10.3390/jimaging10110262