Assessment of lumbar paraspinal muscle morphology using mDixon Quant magnetic resonance imaging (MRI): a cross-sectional study in healthy subjects

. 2024 Aug 01 ; 14 (8) : 6015-6035. [epub] 20240726

Status PubMed-not-MEDLINE Jazyk angličtina Země Čína Médium print-electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39144006

BACKGROUND: Lumbar paraspinal muscles (LPM) are a part of the deep spinal stabilisation system and play an important role in stabilising the lumbar spine and trunk. Inadequate function of these muscles is thought to be an essential aetiological factor in low back pain, and several neuromuscular diseases are characterised by dysfunction of LPM. The main aims of our study were to develop a methodology for LPM assessment using advanced magnetic resonance imaging (MRI) methods, including a manual segmentation process, to confirm the measurement reliability, to evaluate the LPM morphological parameters [fat fraction (FF), total muscle volume (TMV) and functional muscle volume (FMV)] in a healthy population, to study the influence of physiological factors on muscle morphology, and to build equations to predict LPM morphological parameters in a healthy population. METHODS: This prospective cross-sectional observational comparative single-centre study was conducted at the University Hospital in Brno, enrolling healthy volunteers from April 2021 to March 2023. MRI of the lumbar spine and LPM (erector spinae muscle and multifidus muscle) were performed using a 6-point Dixon gradient echo sequence. The segmentation of the LPM and the control muscle (psoas muscle) was done manually to obtain FF and TMV in a range from Th12/L1 to L5/S1. Intra-rater and inter-rater reliability were evaluated. Linear regression models were constructed to assess the effect of physiological factors on muscle FF, TMV and FMV. RESULTS: We enrolled 90 healthy volunteers (median age 38 years, 45 men). The creation of segmentation masks and the assessment of FF and TMV proved reliable (Dice coefficient 84% to 99%, intraclass correlation coefficient ≥0.97). The univariable models showed that FF of LPM was influenced the most by age (39.6% to 44.8% of variability, P<0.001); TMV and FMV by subject weight (34.9% to 67.6% of variability, P<0.001) and sex (24.7% to 64.1% of variability, P<0.001). Multivariable linear regression models for FF of LPM included age, body mass index and sex, with R-squared values ranging from 45.4% to 51.1%. Models for volumes of LPM included weight, age and sex, with R-squared values ranged from 37.4% to 76.8%. Equations were developed to calculate predicted FF, TMV and FMV for each muscle. CONCLUSIONS: A reliable methodology has been developed to assess the morphological parameters (biomarkers) of the LPM. The morphological parameters of the LPM are significantly influenced by physiological factors. Equations were constructed to calculate the predicted FF, TMV and FMV of individual muscles in relation to anthropometric parameters, age, and sex. This study, which presented LPM assessment methodology and predicted values of LPM morphological parameters in a healthy population, could improve our understanding of diseases involving LPM (low back pain and some neuromuscular diseases).

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Panjabi MM. A hypothesis of chronic back pain: ligament subfailure injuries lead to muscle control dysfunction. Eur Spine J 2006;15:668-76. 10.1007/s00586-005-0925-3 PubMed DOI PMC

Freeman MD, Woodham MA, Woodham AW. The role of the lumbar multifidus in chronic low back pain: a review. PM R 2010;2:142-6; quiz 1 p following 167. PubMed

Steele J, Bruce-Low S, Smith D. A reappraisal of the deconditioning hypothesis in low back pain: review of evidence from a triumvirate of research methods on specific lumbar extensor deconditioning. Curr Med Res Opin 2014;30:865-911. 10.1185/03007995.2013.875465 PubMed DOI

Diamanti L, Paoletti M, Vita UD, Muzic SI, Cereda C, Ballante E, Pichiecchio A. MRI study of paraspinal muscles in patients with Amyotrophic Lateral Sclerosis (ALS). J Clin Med 2020;9:934. 10.3390/jcm9040934 PubMed DOI PMC

Witting N, Andersen LK, Vissing J. Axial myopathy: an overlooked feature of muscle diseases. Brain 2016;139:13-22. 10.1093/brain/awv332 PubMed DOI

Kalichman L, Hodges P, Li L, Guermazi A, Hunter DJ. Changes in paraspinal muscles and their association with low back pain and spinal degeneration: CT study. Eur Spine J 2010;19:1136-44. 10.1007/s00586-009-1257-5 PubMed DOI PMC

Carlier PG, Marty B, Scheidegger O, Loureiro de Sousa P, Baudin PY, Snezhko E, Vlodavets D. Skeletal Muscle Quantitative Nuclear Magnetic Resonance Imaging and Spectroscopy as an Outcome Measure for Clinical Trials. J Neuromuscul Dis 2016;3:1-28. 10.3233/JND-160145 PubMed DOI PMC

Barnard AM, Willcocks RJ, Triplett WT, Forbes SC, Daniels MJ, Chakraborty S, Lott DJ, Senesac CR, Finanger EL, Harrington AT, Tennekoon G, Arora H, Wang DJ, Sweeney HL, Rooney WD, Walter GA, Vandenborne K. MR biomarkers predict clinical function in Duchenne muscular dystrophy. Neurology 2020;94:e897-909. 10.1212/WNL.0000000000009012 PubMed DOI PMC

Crawford RJ, Filli L, Elliott JM, Nanz D, Fischer MA, Marcon M, Ulbrich EJ. Age- and Level-Dependence of Fatty Infiltration in Lumbar Paravertebral Muscles of Healthy Volunteers. AJNR Am J Neuroradiol 2016;37:742-8. 10.3174/ajnr.A4596 PubMed DOI PMC

Dahlqvist JR, Vissing CR, Hedermann G, Thomsen C, Vissing J. Fat Replacement of Paraspinal Muscles with Aging in Healthy Adults. Med Sci Sports Exerc 2017;49:595-601. 10.1249/MSS.0000000000001119 PubMed DOI

Khil EK, Choi JA, Hwang E, Sidek S, Choi I. Paraspinal back muscles in asymptomatic volunteers: quantitative and qualitative analysis using computed tomography (CT) and magnetic resonance imaging (MRI). BMC Musculoskelet Disord 2020;21:403. 10.1186/s12891-020-03432-w PubMed DOI PMC

Sollmann N, Zoffl A, Franz D, Syväri J, Dieckmeyer M, Burian E, Klupp E, Hedderich DM, Holzapfel C, Drabsch T, Kirschke JS, Rummeny EJ, Zimmer C, Hauner H, Karampinos DC, Baum T. Regional variation in paraspinal muscle composition using chemical shift encoding-based water-fat MRI. Quant Imaging Med Surg 2020;10:496-507. 10.21037/qims.2020.01.10 PubMed DOI PMC

Lee SH, Park SW, Kim YB, Nam TK, Lee YS. The fatty degeneration of lumbar paraspinal muscles on computed tomography scan according to age and disc level. Spine J 2017;17:81-7. 10.1016/j.spinee.2016.08.001 PubMed DOI

Takayama K, Kita T, Nakamura H, Kanematsu F, Yasunami T, Sakanaka H, Yamano Y. New Predictive Index for Lumbar Paraspinal Muscle Degeneration Associated With Aging. Spine (Phila Pa 1976) 2016;41:E84-90. 10.1097/BRS.0000000000001154 PubMed DOI

Urrutia J, Besa P, Lobos D, Andia M, Arrieta C, Uribe S. Is a single-level measurement of paraspinal muscle fat infiltration and cross-sectional area representative of the entire lumbar spine? Skeletal Radiol 2018;47:939-45. 10.1007/s00256-018-2902-z PubMed DOI

Huang R, Pan F, Kong C, Lu S. Age- and sex-dependent differences in the morphology and composition of paraspinal muscles between subjects with and without lumbar degenerative diseases. BMC Musculoskelet Disord 2022;23:734. 10.1186/s12891-022-05692-0 PubMed DOI PMC

Sasaki T, Yoshimura N, Hashizume H, Yamada H, Oka H, Matsudaira K, Iwahashi H, Shinto K, Ishimoto Y, Nagata K, Teraguchi M, Kagotani R, Muraki S, Akune T, Tanaka S, Kawaguchi H, Nakamura K, Minamide A, Nakagawa Y, Yoshida M. MRI-defined paraspinal muscle morphology in Japanese population: The Wakayama Spine Study. PLoS One 2017;12:e0187765. 10.1371/journal.pone.0187765 PubMed DOI PMC

Zhang Y, Zhou Z, Wang C, Cheng X, Wang L, Duanmu Y, Zhang C, Veronese N, Guglielmi G. Reliability of measuring the fat content of the lumbar vertebral marrow and paraspinal muscles using MRI mDIXON-Quant sequence. Diagn Interv Radiol 2018;24:302-7. 10.5152/dir.2018.17323 PubMed DOI PMC

Han G, Jiang Y, Zhang B, Gong C, Li W. Imaging Evaluation of Fat Infiltration in Paraspinal Muscles on MRI: A Systematic Review with a Focus on Methodology. Orthop Surg 2021;13:1141-8. 10.1111/os.12962 PubMed DOI PMC

Goutallier D, Postel JM, Bernageau J, Lavau L, Voisin MC. Fatty muscle degeneration in cuff ruptures. Pre- and postoperative evaluation by CT scan. Clin Orthop Relat Res 1994;(304):78-83. PubMed

Mercuri E, Counsell S, Allsop J, Jungbluth H, Kinali M, Bonne G, Schwartz K, Bydder G, Dubowitz V, Muntoni F. Selective muscle involvement on magnetic resonance imaging in autosomal dominant Emery-Dreifuss muscular dystrophy. Neuropediatrics 2002;33:10-4. 10.1055/s-2002-23593 PubMed DOI

Solbakken G, Bjørnarå B, Kirkhus E, Nguyen B, Hansen G, Frich JC, Ørstavik K. MRI of trunk muscles and motor and respiratory function in patients with myotonic dystrophy type 1. BMC Neurol 2019;19:135. 10.1186/s12883-019-1357-8 PubMed DOI PMC

Perkins TG, Duijndam A, Eggers H, Weerdt E, Rijckaert YHE. The next generation fat-free imaging. 2015. Available online: https://philipsproductcontent.blob.core.windows.net/assets/20170523/77840f58014b4ea8bc44a77c015697b7.pdf

Burian E, Rohrmeier A, Schlaeger S, Dieckmeyer M, Diefenbach MN, Syväri J, Klupp E, Weidlich D, Zimmer C, Rummeny EJ, Karampinos DC, Kirschke JS, Baum T. Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine. BMC Musculoskelet Disord 2019;20:152. 10.1186/s12891-019-2528-x PubMed DOI PMC

Sjostrom M, Ainsworth B, Bauman A, Bull F, Hamilton-Craig C, Sallis J. Guidelines for data processing analysis of the International Physical Activity Questionnaire (IPAQ) - Short and long forms. 2005. Available online: https://www.semanticscholar.org/paper/Guidelines-for-data-processing-analysis-of-the-and-Sjostrom-Ainsworth/efb9575f5c957b73c640f00950982e618e31a7be

Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006;31:1116-28. 10.1016/j.neuroimage.2006.01.015 PubMed DOI

Crawford RJ, Cornwall J, Abbott R, Elliott JM. Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference. BMC Musculoskelet Disord 2017;18:25. 10.1186/s12891-016-1378-z PubMed DOI PMC

Weinreb JC, Cohen JM, Maravilla KR. Iliopsoas muscles: MR study of normal anatomy and disease. Radiology 1985;156:435-40. 10.1148/radiology.156.2.4011906 PubMed DOI

Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 2015;15:29. 10.1186/s12880-015-0068-x PubMed DOI PMC

Akoglu H. User's guide to correlation coefficients. Turk J Emerg Med 2018;18:91-3. 10.1016/j.tjem.2018.08.001 PubMed DOI PMC

Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 2016;15:155-63. 10.1016/j.jcm.2016.02.012 PubMed DOI PMC

Rummens S, Bosch S, Dierckx S, Vanmechelen A, Peeters R, Brumagne S, Desloovere K, Peers K. Reliability and agreement of lumbar multifidus volume and fat fraction quantification using magnetic resonance imaging. Musculoskelet Sci Pract 2022;59:102532. 10.1016/j.msksp.2022.102532 PubMed DOI

Valentin S, Licka T, Elliott J. Age and side-related morphometric MRI evaluation of trunk muscles in people without back pain. Man Ther 2015;20:90-5. 10.1016/j.math.2014.07.007 PubMed DOI PMC

Dourthe B, Shaikh N, Pai S A, Fels S, Brown SHM, Wilson DR, Street J, Oxland TR. Automated Segmentation of Spinal Muscles From Upright Open MRI Using a Multiscale Pyramid 2D Convolutional Neural Network. Spine (Phila Pa 1976) 2022;47:1179-86. 10.1097/BRS.0000000000004308 PubMed DOI

Li H, Luo H, Liu Y. Paraspinal Muscle Segmentation Based on Deep Neural Network. Sensors (Basel) 2019;19:2650. 10.3390/s19122650 PubMed DOI PMC

Kise Y, Chikui T, Yamashita Y, Kobayashi K, Yoshiura K. Clinical usefulness of the mDIXON Quant the method for estimation of the salivary gland fat fraction: comparison with MR spectroscopy. Br J Radiol 2017;90:20160704. 10.1259/bjr.20160704 PubMed DOI PMC

Kukuk GM, Hittatiya K, Sprinkart AM, Eggers H, Gieseke J, Block W, Moeller P, Willinek WA, Spengler U, Trebicka J, Fischer HP, Schild HH, Träber F. Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis. Eur Radiol 2015;25:2869-79. 10.1007/s00330-015-3703-6 PubMed DOI

Baum T, Lorenz C, Buerger C, Freitag F, Dieckmeyer M, Eggers H, Zimmer C, Karampinos DC, Kirschke JS. Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images. Eur Radiol Exp 2018;2:32. 10.1186/s41747-018-0065-2 PubMed DOI PMC

Modesto AE, Stuart CE, Cho J, Ko J, Singh RG, Petrov MS. Psoas muscle size as a magnetic resonance imaging biomarker of progression of pancreatitis. Eur Radiol 2020;30:2902-11. 10.1007/s00330-019-06633-7 PubMed DOI

Kasukawa Y, Hongo M, Ebina T, Chiba T, Kudo D, Kimura R, Shimada Y, Miyakoshi N. Quantitative Evaluation of Fat Composition in Lumbar Vertebral Body and Paraspinal Muscle by Proton Density Fat Fraction with MRI. Open Journal of Orthopedics 2022;12:85-96.

Shahidi B, Parra CL, Berry DB, Hubbard JC, Gombatto S, Zlomislic V, Allen RT, Hughes-Austin J, Garfin S, Ward SR. Contribution of Lumbar Spine Pathology and Age to Paraspinal Muscle Size and Fatty Infiltration. Spine (Phila Pa 1976) 2017;42:616-23. 10.1097/BRS.0000000000001848 PubMed DOI PMC

Fortin M, Videman T, Gibbons LE, Battié MC. Paraspinal muscle morphology and composition: a 15-yr longitudinal magnetic resonance imaging study. Med Sci Sports Exerc 2014;46:893-901. 10.1249/MSS.0000000000000179 PubMed DOI

Burian E, Inhuber S, Schlaeger S, Dieckmeyer M, Klupp E, Franz D, Weidlich D, Sollmann N, Löffler M, Schwirtz A, Rummeny EJ, Zimmer C, Kirschke JS, Karampinos DC, Baum T. Association of thigh and paraspinal muscle composition in young adults using chemical shift encoding-based water-fat MRI. Quant Imaging Med Surg 2020;10:128-36. 10.21037/qims.2019.11.08 PubMed DOI PMC

Vlažná D, Krkoška P, Kuhn M, Dosbaba F, Batalik L, Vlčková E, Voháňka S, Adamová B. Assessment of Lumbar Extensor Muscles in the Context of Trunk Function, a Pilot Study in Healthy Individuals. Appl Sci 2021;11:9518.

Mannion AF. Fibre type characteristics and function of the human paraspinal muscles: normal values and changes in association with low back pain. J Electromyogr Kinesiol 1999;9:363-77. 10.1016/s1050-6411(99)00010-3 PubMed DOI

Arbanas J, Klasan GS, Nikolic M, Jerkovic R, Miljanovic I, Malnar D. Fibre type composition of the human psoas major muscle with regard to the level of its origin. J Anat 2009;215:636-41. 10.1111/j.1469-7580.2009.01155.x PubMed DOI PMC

Steele J, Fisher J, Perrin C, Conway R, Bruce-Low S, Smith D. Does change in isolated lumbar extensor muscle function correlate with good clinical outcome? A secondary analysis of data on change in isolated lumbar extension strength, pain, and disability in chronic low back pain. Disabil Rehabil 2019;41:1287-95. 10.1080/09638288.2018.1424952 PubMed DOI

Krkoska P, Vlazna D, Sladeckova M, Minarikova J, Barusova T, Batalik L, Dosbaba F, Vohanka S, Adamova B. Adherence and Effect of Home-Based Rehabilitation with Telemonitoring Support in Patients with Chronic Non-Specific Low Back Pain: A Pilot Study. Int J Environ Res Public Health 2023;20:1504. 10.3390/ijerph20021504 PubMed DOI PMC

Csapo R, Malis V, Sinha U, Du J, Sinha S. Age-associated differences in triceps surae muscle composition and strength - an MRI-based cross-sectional comparison of contractile, adipose and connective tissue. BMC Musculoskelet Disord 2014;15:209. 10.1186/1471-2474-15-209 PubMed DOI PMC

Kader DF, Wardlaw D, Smith FW. Correlation between the MRI changes in the lumbar multifidus muscles and leg pain. Clin Radiol 2000;55:145-9. 10.1053/crad.1999.0340 PubMed DOI

Parkkola R, Rytökoski U, Kormano M. Magnetic resonance imaging of the discs and trunk muscles in patients with chronic low back pain and healthy control subjects. Spine (Phila Pa 1976) 1993;18:830-6. 10.1097/00007632-199306000-00004 PubMed DOI

Hu ZJ, He J, Zhao FD, Fang XQ, Zhou LN, Fan SW. An assessment of the intra- and inter-reliability of the lumbar paraspinal muscle parameters using CT scan and magnetic resonance imaging. Spine (Phila Pa 1976) 2011;36:E868-74. 10.1097/BRS.0b013e3181ef6b51 PubMed DOI

Ranson CA, Burnett AF, Kerslake R, Batt ME, O'Sullivan PB. An investigation into the use of MR imaging to determine the functional cross sectional area of lumbar paraspinal muscles. Eur Spine J 2006;15:764-73. 10.1007/s00586-005-0909-3 PubMed DOI PMC

Kim TN, Choi KM. Sarcopenia: definition, epidemiology, and pathophysiology. J Bone Metab 2013;20:1-10. 10.11005/jbm.2013.20.1.1 PubMed DOI PMC

Wiedmer P, Jung T, Castro JP, Pomatto LCD, Sun PY, Davies KJA, Grune T. Sarcopenia - Molecular mechanisms and open questions. Ageing Res Rev 2021;65:101200. 10.1016/j.arr.2020.101200 PubMed DOI

Volpi E, Nazemi R, Fujita S. Muscle tissue changes with aging. Curr Opin Clin Nutr Metab Care 2004;7:405-10. 10.1097/01.mco.0000134362.76653.b2 PubMed DOI PMC

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