Sonographic image texture features in muscle tissue-mimicking material reduce variability introduced by probe angle and gain settings compared to traditional echogenicity

Authors

  • Dustin J. Oranchuk Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Physical Medicine and Rehabilitation, University of Colorado, Anschutz Medical Campus, Aurora, Colorado https://orcid.org/0000-0003-4489-9022
  • Katie L. Boncella Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado
  • Daniella Gonzalez-Rivera Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; University of Colorado Physical Therapy Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
  • Michael O. Harris-Love Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Physical Medicine and Rehabilitation, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States; University of Colorado Physical Therapy Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Eastern Colorado VA Geriatric Research, Education, and Clinical Center, Aurora, Colorado

DOI:

https://doi.org/10.4081/ejtm.2025.13511

Keywords:

Phantom, echo intensity, gray level of co-occurrence matrix, muscle tissue mimetic, ultrasound

Abstract

Cost-effective and portable ultrasonography offers a promising approach for monitoring skeletal muscle damage and quality in many contexts. However, echogenicity analysis relies on precise transducer orientations and machine parameters, posing challenges for data pooling across different raters and settings. Muscle texture analysis offers a potential means of reducing inter-rater and machine-setting variability. Scans were assessed at nine angles, controlled using a custom transducer shell and software. Scans were performed three times, and different gains were applied. All scans were performed on a muscle tissue-mimicking phantom to eliminate biological variability. Intra-angle and intra-gain variability and internal consistency were assessed via coefficient of variation (CV%) and Cronbach's alpha (αc). Spearman's (ρ) correlations were employed to determine the relationship between echogenicity and each texture feature. Entropy (angle: CV=2.7-7.6%; gain: CV=10.5%; αc=0.86), and inverse difference moment (angle: CV=3.7-9.8%; gain: CV=16.5%; αc=0.87) were less variable than echogenicity (angle: CV=6.4-19.4%; gain: CV=39.0%; αc=0.82). Angular second moment (angle: CV=17.9-116.6%; gain: CV=71.6%; αc=0.68), contrast (angle: CV=7.8-14.7%; gain: CV=41.8%;αc=0.75), and correlation (angle: CV=9.0-13.5%; gain: CV=28.6%; αc=0.49) features were generally more variable. Entropy (ρ=0.82–0.98, p≤0.011) and inverse difference moment (ρ=-0.98–-0.83, p≤0.008), were more strongly correlated with echogenicity than angular second moment (ρ=-0.98–-0.77, p≤0.016), contrast (ρ=0.53–0.98, p≤0.15), and correlation (ρ=-0.25–-0.19, p=0.520-0.631). Entropy and inverse difference moment features may allow data sharing between laboratory and clinical settings with ultrasound machine parameters and raters of varying skill levels. Clinical and mechanistic studies are required to determine if texture features can replace echogenicity assessments.

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References

Stock MS, Thompson BJ. Echo intensity as an indicator of skeletal muscle quality: applications, methodology, and future directions. Eur J Appl Physiol 2021;121:369-80. DOI: https://doi.org/10.1007/s00421-020-04556-6

Paris M, Mourtzakis M. Muscle composition analysis of ultrasound images: A narrative review of texture analysis. Ultrasound Med Biol 2021;47:880-95. DOI: https://doi.org/10.1016/j.ultrasmedbio.2020.12.012

Wong V, Spitz RW, Bell ZW, et al. Exercise induced changes in echo intensity within the muscle: A brief review. J Ultrasound 2020;23:457-72. DOI: https://doi.org/10.1007/s40477-019-00424-y

Young H, Jenkins NT, Zhao Q, McCully KK. Measurement of intramuscular fat by muscle echo intensity. Muscle Nerve 2015;52:963-71. DOI: https://doi.org/10.1002/mus.24656

Pillen S, Tak RO, Zwarts MJ, et al. Skeletal muscle ultrasound: Correlation between fibrous tissue and echo intensity. Ultrasound Med Biol 2009;35:443-6. DOI: https://doi.org/10.1016/j.ultrasmedbio.2008.09.016

Oranchuk DJ, Stock MS, Nelson AR, et al. Variability of regional quadriceps echo intensity in active young men with and without subcutaneous fat correction. Appl Physiol Nutr Metab 2020;45:745-52. DOI: https://doi.org/10.1139/apnm-2019-0601

Oranchuk DJ, Bodkin SG, Boncella KL, Harris-Love MO. Exploring the associations between skeletal muscle echogenicity and physical function in aging adults: A systematic review with meta-analyses. J Sport Health Sci 2024;13:820-40. DOI: https://doi.org/10.1016/j.jshs.2024.05.005

Zhang YN, Fowler KJ, Hamilton G, et al. Liver fat imaging—A clinical overview of ultrasound, CT, and MR imaging. Br J Radiol 2018;91:20170959. DOI: https://doi.org/10.1259/bjr.20170959

Dankel SJ, Abe T, Bell ZW, et al. The impact of ultrasound probe tilt on muscle thickness and echo-intensity: A cross-sectional study. J Clin Densitom 2020;23:630-8. DOI: https://doi.org/10.1016/j.jocd.2018.10.003

Varanoske AN, Coker NA, Johnson BAD, Belity T, Wells AJ. Influence of muscle depth and thickness on ultrasound echo intensity of the vastus lateralis. Acta Radiol. 2021;62:1178-87. DOI: https://doi.org/10.1177/0284185120958405

Wu JS, Darras BT, Rutkove SB. Assessing spinal muscular atrophy with quantitative ultrasound. Neurology 2010;75:526-31. DOI: https://doi.org/10.1212/WNL.0b013e3181eccf8f

Pinto RS, Pinto MD. Moving forward with the echo intensity mean analysis: Exploring echo intensity bands in different age groups. Exp Gerontol 2021;145:111179. DOI: https://doi.org/10.1016/j.exger.2020.111179

Hobson‐Webb LD, Mhoon JT, Juel VC. Effect of transducer frequency on muscle luminosity ratio. Muscle Nerve 2011;44:612-3. DOI: https://doi.org/10.1002/mus.22155

Logeson ZS, MacLennan RJ, Abad GKB, et al. The impact of skeletal muscle disuse on distinct echo intensity bands: A retrospective analysis. PLoS ONE 2022;17:e0262553. DOI: https://doi.org/10.1371/journal.pone.0262553

Southhall, K, Wohlgemuth KJ, Hare MM, Mota JA. Does the analysis of separate bands of echo intensity strengthen the relationship to muscle function? Int J Exerc Sci 2023;2:50.

Harris‐Love MO, Gonzales TI, Wei Q, et al. Association between muscle strength and modeling estimates of muscle tissue heterogeneity in young and old Adults. J Ultrasound Medicine 2019;38:1757-68. DOI: https://doi.org/10.1002/jum.14864

Fuentes-Abolafio IJ, Ricci M, Bernal-López MR, et al. Relationship between quadriceps femoris echotexture biomarkers and muscle strength and physical function in older adults with heart failure with preserved ejection fraction. Exp Gerontol 2024;190:112412. DOI: https://doi.org/10.1016/j.exger.2024.112412

Martínez-Payá JJ, Ríos-Díaz J, Del Baño-Aledo ME, et al. Quantitative muscle ultrasonography using textural analysis in amyotrophic lateral sclerosis. Ultrason Imaging 2017;39:357-68. DOI: https://doi.org/10.1177/0161734617711370

Gilbertson MW, Anthony BW. An ergonomic, instrumented ultrasound probe for 6-axis force/torque measurement. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2013:140-3. DOI: https://doi.org/10.1109/EMBC.2013.6609457

Huang AY. May the force be with you: a medical ultrasound system with integrated force measurement. Master of Science in Mechanical Engineering. Massachusetts Institute of Technology. Available from: http://hdl.handle.net/1721.1/113755

Devore JL. Probability and Statistics for Engineering and the Sciences. Ninth edition. Cengage Learning; 2016.

Streiner DL. Starting at the beginning: An introduction to coefficient alpha and internal consistency. J Pers Asses 2003;80:99-103. DOI: https://doi.org/10.1207/S15327752JPA8001_18

Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive Statistics for Studies in Sports Medicine and Exercise Science. Med Sci Sports Exerc 2009;41:3-12. DOI: https://doi.org/10.1249/MSS.0b013e31818cb278

Wilkinson TJ, Ashman J, Baker LA, et al. Quantitative muscle ultrasonography using 2D textural analysis: A novel approach to assess skeletal muscle structure and quality in chronic kidney disease. Ultrason Imaging 2021;43:139-48. DOI: https://doi.org/10.1177/01617346211009788

Watanabe T, Murakami H, Fukuoka D, et al. Quantitative sonographic assessment of the quadriceps femoris muscle in healthy Japanese adults. J Ultrasound Med 2017;36:1383-95. DOI: https://doi.org/10.7863/ultra.16.07054

Oranchuk DJ, Nelson AR, Storey AG, et al. Short-term neuromuscular, morphological, and architectural responses to eccentric quasi-isometric muscle actions. Eur J Appl Physiol 2021;121:141-58. DOI: https://doi.org/10.1007/s00421-020-04512-4

Radaelli R, Bottaro M, Wilhelm EN, et al. Time course of strength and echo intensity recovery after resistance exercise in women. J Strength Cond Res 2012;26:2577-84. DOI: https://doi.org/10.1519/JSC.0b013e31823dae96

Matta TTD, Pereira WCDA, Radaelli R, et al. Texture analysis of ultrasound images is a sensitive method to follow‐up muscle damage induced by eccentric exercise. Clin Physio Funct Imaging 2018;38:477-82. DOI: https://doi.org/10.1111/cpf.12441

Jo HD, Kim MK. Identification of EIMD level differences between long- and short head of biceps brachii using echo intensity and GLCM texture features. Res Q Exerc Sport 2024;95:441-9. DOI: https://doi.org/10.1080/02701367.2023.2250832

Mason CN, Miller T. International projections of age specific healthcare consumption: 2015–2060. J Econ Ageing 2018;12:202-17. DOI: https://doi.org/10.1016/j.jeoa.2017.04.003

Akinrolie O, Iwuagwu AO, Kalu ME, et al. Longitudinal studies of aging in sub-saharan Africa: Review, limitations, and recommendations in preparation of projected aging population. Chukwuorji J, ed. Innov Aging 2024;8:igae002. DOI: https://doi.org/10.1093/geroni/igae002

Maghsoud F, Rezaei M, Asgarian FS, Rassouli M. Workload and quality of nursing care: The mediating role of implicit rationing of nursing care, job satisfaction and emotional exhaustion by using structural equations modeling approach. BMC Nurs 2022;21:273. DOI: https://doi.org/10.1186/s12912-022-01055-1

Published

01-04-2025

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Section

Articles | AI for Mobility Medicine

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How to Cite

Sonographic image texture features in muscle tissue-mimicking material reduce variability introduced by probe angle and gain settings compared to traditional echogenicity. (2025). European Journal of Translational Myology, 35(2). https://doi.org/10.4081/ejtm.2025.13511