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 Fortin; Melinda 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

doi:10.3390/jimaging10110262

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 journal › Review article › peer-review

37 Downloads (Pure)

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 language	English
Article number	262
Pages (from-to)	1-17
Number of pages	17
Journal	Journal of Imaging
Volume	10
Issue number	11
DOIs	https://doi.org/10.3390/jimaging10110262
Publication status	Published - 22 Oct 2024

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

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

Access to Document

10.3390/jimaging10110262Licence: CC BY

Publisher version (open access)Final published version, 1.55 MBLicence: CC BY

Cite this

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., ... Elliott, J. M. (2024). MuscleMap: an open-source, community-supported consortium for whole-body quantitative MRI of muscle. Journal of Imaging, 10(11), 1-17. Article 262. https://doi.org/10.3390/jimaging10110262

@article{9ce663bbfeae423cbd997e47f20d48e2,

title = "MuscleMap: an open-source, community-supported consortium for whole-body quantitative MRI of muscle",

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 {\textquoteleft}typical{\textquoteright} age-related muscle composition is essential to accurately identify and evaluate what is {\textquoteleft}atypical{\textquoteright}. 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 = "artificial intelligence, machine learning, MR imaging, muscle fat infiltration, neural networks, normative reference data, public datasets",

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

note = "Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

year = "2024",

month = oct,

day = "22",

doi = "10.3390/jimaging10110262",

language = "English",

volume = "10",

pages = "1--17",

journal = "Journal of Imaging",

issn = "2313-433X",

publisher = "Multidisciplinary Digital Publishing Institute",

number = "11",

}

McKay, MJ, Weber, KA, Wesselink, EO, Smith, ZA, Abbott, R, Anderson, DB, Ashton-James, CE, Atyeo, J, Beach, AJ, Burns, J, Clarke, S, Collins, NJ, Coppieters, MW, Cornwall, J, Crawford, RJ, De Martino, E, Dunn, AG, Eyles, JP, Feng, HJ, Fortin, M, Franettovich Smith, MM, Galloway, G, Gandomkar, Z, Glastras, S, Henderson, LA, Hides, JA, Hiller, CE, Hilmer, SN, Hoggarth, MA, Kim, B, Lal, N, LaPorta, L, Magnussen, JS, Maloney, S, March, L, Nackley, AG, O’Leary, SP, Peolsson, A, Perraton, Z, Pool-Goudzwaard, AL, Schnitzler, M, Seitz, AL, Semciw, AI, Sheard, PW, Smith, AC, Snodgrass, SJ, Sullivan, J, Tran, V, Valentin, S, Walton, DM, Wishart, LR & Elliott, JM 2024, 'MuscleMap: an open-source, community-supported consortium for whole-body quantitative MRI of muscle', Journal of Imaging, vol. 10, no. 11, 262, pp. 1-17. https://doi.org/10.3390/jimaging10110262

TY - JOUR

T1 - MuscleMap

T2 - an open-source, community-supported consortium for whole-body quantitative MRI of muscle

AU - McKay, Marnee J.

AU - Weber, Kenneth A.

AU - Wesselink, Evert O.

AU - Smith, Zachary A.

AU - Abbott, Rebecca

AU - Anderson, David B.

AU - Ashton-James, Claire E.

AU - Atyeo, John

AU - Beach, Aaron J.

AU - Burns, Joshua

AU - Clarke, Stephen

AU - Collins, Natalie J.

AU - Coppieters, Michel W.

AU - Cornwall, Jon

AU - Crawford, Rebecca J.

AU - De Martino, Enrico

AU - Dunn, Adam G.

AU - Eyles, Jillian P.

AU - Feng, Henry J.

AU - Fortin, Maryse

AU - Franettovich Smith, Melinda M.

AU - Galloway, Graham

AU - Gandomkar, Ziba

AU - Glastras, Sarah

AU - Henderson, Luke A.

AU - Hides, Julie A.

AU - Hiller, Claire E.

AU - Hilmer, Sarah N.

AU - Hoggarth, Mark A.

AU - Kim, Brian

AU - Lal, Navneet

AU - LaPorta, Laura

AU - Magnussen, John S.

AU - Maloney, Sarah

AU - March, Lyn

AU - Nackley, Andrea G.

AU - O’Leary, Shaun P.

AU - Peolsson, Anneli

AU - Perraton, Zuzana

AU - Pool-Goudzwaard, Annelies L.

AU - Schnitzler, Margaret

AU - Seitz, Amee L.

AU - Semciw, Adam I.

AU - Sheard, Philip W.

AU - Smith, Andrew C.

AU - Snodgrass, Suzanne J.

AU - Sullivan, Justin

AU - Tran, Vienna

AU - Valentin, Stephanie

AU - Walton, David M.

AU - Wishart, Laurelie R.

AU - Elliott, James M.

N1 - Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2024/10/22

Y1 - 2024/10/22

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - machine learning

KW - MR imaging

KW - muscle fat infiltration

KW - neural networks

KW - normative reference data

KW - public datasets

UR - http://www.scopus.com/inward/record.url?scp=85210555561&partnerID=8YFLogxK

U2 - 10.3390/jimaging10110262

DO - 10.3390/jimaging10110262

M3 - Review article

C2 - 39590726

AN - SCOPUS:85210555561

SN - 2313-433X

VL - 10

SP - 1

EP - 17

JO - Journal of Imaging

JF - Journal of Imaging

IS - 11

M1 - 262

ER -

MuscleMap: an open-source, community-supported consortium for whole-body quantitative MRI of muscle

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Cite this