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

Marnee J McKay¹, Kenneth A Weber 2nd², Evert O Wesselink^{2

3}, Zachary A Smith⁴, Rebecca Abbott⁵, David B Anderson¹, Claire E Ashton-James¹, John Atyeo¹, Aaron J Beach⁶, Joshua Burns⁷, Stephen Clarke¹, Natalie J Collins⁸, Michel W Coppieters⁹, Jon Cornwall¹⁰, Rebecca J Crawford¹¹, Enrico De Martino¹², Adam G Dunn¹, Jillian P Eyles^{1

13}, Henry J Feng¹, Maryse Fortin¹⁴, Melinda M Franettovich Smith⁸, Graham Galloway¹⁵, Ziba Gandomkar¹, Sarah Glastras^{1

13}, Luke A Henderson¹, Julie A Hides⁹, Claire E Hiller¹, Sarah N Hilmer¹, Mark A Hoggarth¹⁶, Brian Kim^{1

13}, Navneet Lal¹⁰, Laura LaPorta¹⁷, John S Magnussen⁶, Sarah Maloney¹, Lyn March¹, Andrea G Nackley¹⁸, Shaun P O'Leary⁸, Anneli Peolsson^{19

20}, Zuzana Perraton²¹, Annelies L Pool-Goudzwaard³, Margaret Schnitzler¹, Amee L Seitz²², Adam I Semciw²¹, Philip W Sheard¹⁰, Andrew C Smith²³, Suzanne J Snodgrass²⁴, Justin Sullivan¹, Vienna Tran²⁵, Stephanie Valentin²⁶, David M Walton²⁷, Laurelie R Wishart^{8

28}, James M Elliott^{1

13}

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.

PMID: 39590726
PMCID: PMC11595196
DOI: 10.3390/jimaging10110262

Review

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

Marnee J McKay et al. J Imaging. 2024.

. 2024 Oct 22;10(11):262.

doi: 10.3390/jimaging10110262.

Authors

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.

PMID: 39590726
PMCID: PMC11595196
DOI: 10.3390/jimaging10110262

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.

Keywords: MR imaging; artificial intelligence; machine learning; muscle fat infiltration; neural networks; normative reference data; public datasets.

PubMed Disclaimer

Conflict of interest statement

Rebecca Abbott reports that financial support was provided by the National Institutes of Health. Kenneth A. Weber II reports that financial support was provided by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health. Adam G. Dunn reports a consulting or advisory relationship with MoleMap. James M. Elliott reports a consulting or advisory relationship with Orofacial Therapeutics LP. James M. Elliott reports a relationship with Medbridge that includes speaking and lecture fees. Andrea G. Nackley reports a relationship with USASP BOD that includes board membership. Andrew C. Smith reports a relationship with Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health that includes funding grants. Anneli Peolsson reports a relationship with the Swedish Research Council that includes funding grants. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

**Figure 1**
(A) Axial cervical spine muscle segmentations at the C4 vertebral level from manual segmentation and an automated computer-vision model overlaid over a water image from Dixon fat–water MRI. (B) Three-dimensional renderings of cervical spine muscle segmentations. The muscle groups segmented include the multifidus and semispinalis cervicis (left = light pink, right = aqua), longus colli and longus capitis (left = light green, right = gold), semispinalis capitis (left = orange, right = yellow), splenius capitis (left = dark pink, right = light blue), levator scapula (left = indigo, right = dark green), sternocleidomastoid (left = blue, right = red), and trapezius (left = brown, right = magenta). L = left, R = right, A = anterior, P = posterior, S = superior, I = inferior. Adapted from Weber et al., 2021 [5].

**Figure 2**
(A) Axial lumbar spine muscle segmentations at the L4 vertebral level from manual segmentation and an automated computer-vision model overlaid over a spin-echo T₂-weighted image. (B) Three-dimensional renderings of the lumbar spine muscle segmentations. The muscle groups segmented include the multifidus (left = light orange, right = dark orange), erector spinae (left = light blue, right = dark blue), and psoas major (left = light green, right = dark green). L = left, R = right, A = anterior, P = posterior, S = superior, I = inferior. Adapted from Wesselink et al., 2022 [6].

**Figure 3**
(A) Axial right hip muscle segmentation overlaid over a fat image from Dixon fat–water MRI. (B) Three-dimensional renderings of the hip muscle segmentations. The muscle groups segmented include the gluteus maximus (blue), gluteus medius (green), and gluteus minimus (red). Adapted from Perraton et al., 2024 [69].

**Figure 4**
Three-dimensional renderings of the intrinsic foot muscles from Dixon fat–water imaging. The muscle groups segmented included the abductor hallucis (plum), quadratus plantae (light blue), flexor digitorum brevis (fuchsia), abductor digiti minimi (orange), lumbricals (yellow), extensor digitorum brevis (pink), flexor hallucis brevis medial head (red), flexor hallucis brevis lateral head (salmon), adductor hallucis (dark blue), flexor digiti minimi (purple), and plantar and dorsal interossei (green). Adapted from Franettovich et al., 2021 [80].

See this image and copyright information in PMC

References

1. Lube J., Cotofana S., Bechmann I., Milani T.L., Ozkurtul O., Sakai T., Stenke H., Hammer N. Reference data on muscle volumes of healthy human pelvis and lower extremity muscles: An in vivo magnetic resonance imaging feasibility study. Surg. Radiol. Anat. 2016;38:97–106. doi: 10.1007/s00276-015-1526-4. - DOI - PubMed
1. Marcon M., Berger N., Manoliu A., Fischer M.A., Nanz D., Andreisek G., Ulbrich E.J. Normative values for volume and fat content of the hip abductor muscles and their dependence on side, age and gender in a healthy population. Skelet. Radiol. 2016;45:465–474. doi: 10.1007/s00256-015-2325-z. - DOI - PubMed
1. Ulbrich E.J., Nanz D., Leinhard O.D., Marcon M., Fischer M.A. Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population. Magn. Reson. Med. 2018;79:449–458. doi: 10.1002/mrm.26676. - DOI - PubMed
1. Valentin S., Licka T., Elliott J. Age and side-related morphometric MRI evaluation of trunk muscles in people without back pain. Man. Ther. 2015;20:90–95. doi: 10.1016/j.math.2014.07.007. - DOI - PMC - PubMed
1. Weber K.A., Abbott R., Bojilov V., Smith A.C., Wasielewski M., Hastie T.J., Parrish T.B., Mackey S., Elliott J.M. Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions. Sci. Rep. 2021;11:16567. doi: 10.1038/s41598-021-95972-x. - DOI - PMC - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

Grants and funding

LinkOut - more resources

Full Text Sources