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21.11.2020 | Magnetic Resonance

Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases

verfasst von: Harmen Reyngoudt, Benjamin Marty, Jean-Marc Boisserie, Julien Le Louër, Cedi Koumako, Pierre-Yves Baudin, Brenda Wong, Tanya Stojkovic, Anthony Béhin, Teresa Gidaro, Yves Allenbach, Olivier Benveniste, Laurent Servais, Pierre G. Carlier

Erschienen in: European Radiology | Ausgabe 6/2021

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Abstract

Objectives

Magnetic resonance imaging (MRI) constitutes a powerful outcome measure in neuromuscular disorders, yet there is a broad diversity of approaches in data acquisition and analysis. Since each neuromuscular disease presents a specific pattern of muscle involvement, the recommended analysis is assumed to be the muscle-by-muscle approach. We, therefore, performed a comparative analysis of different segmentation approaches, including global muscle segmentation, to determine the best strategy for evaluating disease progression.

Methods

In 102 patients (21 immune-mediated necrotizing myopathy/IMNM, 21 inclusion body myositis/IBM, 10 GNE myopathy/GNEM, 19 Duchenne muscular dystrophy/DMD, 12 dysferlinopathy/DYSF, 7 limb-girdle muscular dystrophy/LGMD2I, 7 Pompe disease, 5 spinal muscular atrophy/SMA), two MRI scans were obtained at a 1-year interval in thighs and lower legs. Regions of interest (ROIs) were drawn in individual muscles, muscle groups, and the global muscle segment. Standardized response means (SRMs) were determined to assess sensitivity to change in fat fraction (ΔFat%) in individual muscles, muscle groups, weighted combinations of muscles and muscle groups, and in the global muscle segment.

Results

Global muscle segmentation gave high SRMs for ΔFat% in thigh and lower leg for IMNM, DYSF, LGMD2I, DMD, SMA, and Pompe disease, and only in lower leg for GNEM and thigh for IBM.

Conclusions

Global muscle segment Fat% showed to be sensitive to change in most investigated neuromuscular disorders. As compared to individual muscle drawing, it is a faster and an easier approach to assess disease progression. The use of individual muscle ROIs, however, is still of interest for exploring selective muscle involvement.

Key Points

• MRI-based evaluation of fatty replacement in muscles is used as an outcome measure in the assessment of 1-year disease progression in 8 different neuromuscular diseases.

• Different segmentation approaches, including global muscle segmentation, were evaluated for determining 1-year fat fraction changes in lower limb skeletal muscles.

• Global muscle segment fat fraction has shown to be sensitive to change in lower leg and thigh in most of the investigated neuromuscular diseases.

Carlier PG, Marty B, Scheidegger O et al (2016) Skeletal muscle quantitative nuclear magnetic resonance imaging and spectroscopy as an outcome measure for clinical trials. J Neuromuscul Dis 3:1–28PubMedPubMedCentral

Triplett WT, Baligand C, Forbes SC et al (2014) Chemical shift-based MRI to measure fat fractions in dystrophic skeletal muscle. Magn Reson Med 72:8–19PubMed

Wokke BH, van den Bergen JC, Versluis MJ et al (2014) Quantitative MRI and strength measurements in the assessment of muscle quality in Duchenne muscular dystrophy. Neuromuscul Disord 24:409–416PubMed

Bonati U, Hafner P, Schädelin S et al (2015) Quantitative muscle MRI: a powerful surrogate outcome measure in Duchenne muscular dystrophy. Neuromuscul Disord 25:679–685PubMed

Wary C, Azzabou N, Giraudeau C et al (2015) Quantitative NMRI and NMRS identify augmented disease progression after loss of ambulation in forearms of boys with Duchenne muscular dystrophy. NMR Biomed 28:1150–1162PubMed

Gaeta M, Messina S, Mileto A et al (2012) Muscle fat-fraction and mapping in Duchenne muscular dystrophy: evaluation of disease distribution and correlation with clinical assessments preliminary experience. Skeletal Radiol 41:955–961PubMed

Willis TA, Hollingsworth KG, Coombs A et al (2013) Quantitative muscle MRI as an assessment tool for monitoring disease progression in LGMD2I: a multicentre longitudinal study. PLoS One 8:e70993PubMedPubMedCentral

Murphy AP, Morrow J, Dahlqvist JR et al (2019) Natural history of limb girdle muscular dystrophy R9 over 6 years: searching for trial endpoints. Ann Clin Transl Neurol 6:1033–1045PubMedPubMedCentral

Paradas C, Moore U, James M et al (2016) Clinical outcome study for dysferlinopathy: one-year follow-up. Neuromuscul Disord 26:92–93

10.

Smith FE, Wilson I, Torron R et al (2017) Quantitative magnetic resonance imaging of the skeletal muscle in a multi-center dysferlinopathy study: two-year follow-up. Magn Reson Mater Phys Biol Med 30:63

11.

Diaz-Manera J, Fernandez-Torron R, LLauger J et al (2018) Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials. J Neurol Neurosurg Psychiatry 89:1071–1081PubMedPubMedCentral

12.

Chabanon A, Seferian AM, Daron A et al (2018) Prospective and longitudinal natural history study of patients with type 2 and 3 spinal muscular atrophy: baseline data NatHis-SMA study. PLoS One 13:e0201004PubMedPubMedCentral

13.

Gidaro T, Reyngoudt H, Le Louër J et al (2020) Quantitative nuclear magnetic resonance imaging detects subclinical changes over 1 year in skeletal muscle of GNE myopathy. J Neurol 267:228–238PubMed

14.

Carlier PG, Azzabou N, de Sousa PL et al (2015) Skeletal muscle quantitative nuclear magnetic resonance imaging follow-up of adult Pompe patients. J Inherit Metab Dis 38:565–572PubMedPubMedCentral

15.

Nunez-Peralta C, Alonso-Perez A, Llauger J et al (2020) Follow-up of late-onset Pompe disease patients with muscle magnetic resonance imaging reveals increase in fat replacement in skeletal muscles. J Cachexia Sarcopenia Muscle. https://doi.org/10.1002/jcsm.12555

16.

Morrow JM, Sinclair CDJ, Fischmann A et al (2016) MRI biomarker assessment of neuromuscular disease progression: a prospective observational cohort study. Lancet Neurol 15:65–77PubMedPubMedCentral

17.

Bachasson D, Reyngoudt H, Turk S, Benveniste O, Hogrel JY, Carlier PG (2017) Muscle alterations in sporadic inclusion body myositis assessed using quantitative nuclear magnetic resonance imaging and spectroscopy, ultrasound shear-wave elastography, and relationships with muscle function. Neuromuscul Disord 27:S123

18.

Landon-Cardinal O, Koumako C, Hardouin G et al (2020) Severe axial and pelvifemoral muscle damage in immune-mediated necrotizing myopathy evaluated by whole-body MRI. Semin Arthritis Rheum https://doi.org/10.1016/j.semarthrit.2020.02.009

19.

Wokke BH, van den Bergen JC, Hooijmans MT, Verschuuren JJ, Niks EH, Kan HE (2015) T₂ relaxation times are increased in skeletal muscle of DMD but not BMD patients. Muscle Nerve 53:38–43PubMed

20.

Hooijmans MT, Niks EH, Burakiewicz J, Verschuuren JJGM, Webb AG, Kan HE (2017) Elevated phosphodiester and T₂ levels can be measured in the absence of fat infiltration in Duchenne muscular dystrophy patients. NMR Biomed 30:e3667

21.

Mankodi A, Bishop CA, Auh S, Newbould RD, Fischbeck KH, Janiczek RL (2016) Quantifying disease activity in fatty-infiltrated skeletal muscle by IDEAL-CPMG in Duchenne muscular dystrophy. Neuromuscul Disord 26:650–658PubMedPubMedCentral

22.

Marty B, Coppa B, Carlier PG (2018) Monitoring skeletal muscle chronic fatty degenerations using fast NMR T₁-mapping. Eur Radiol 28:4662–4668PubMed

23.

Ricotti V, Evans MR, Sinclair CD et al (2016) Upper limb evaluation in Duchenne muscular dystrophy: fat-water quantification by MRI, muscle force and function define endpoints for clinical trials. PLoS One 11:e0162542PubMedPubMedCentral

24.

Gerhalter T, Gast LV, Marty B et al (2019) ²³Na MRI depicts early changes in ion homeostasis in skeletal muscle tissue of patients with Duchenne muscular dystrophy. J Magn Reson Imaging 50:1103–1113PubMed

25.

Naarding KJ, Reyngoudt H, van Zwet EW et al (2020) MRI vastus lateralis fat fraction predicts loss of ambulation in Duchenne muscular dystrophy. Neurology 94:e1386–e1394PubMedPubMedCentral

26.

Andersen G, Dahlqvist JR, Vissing CR, Heje K, Thomsen C, Vissing J (2017) MRI as outcome measure in facioscapulohumeral muscular dystrophy: 1-year follow-up of 45 patients. J Neurol 264:438–447PubMed

27.

Arrigoni F, De Luca A, Velardo D et al (2018) Multiparametric quantitative MRI assessment of thigh muscles in limb-girdle muscular dystrophy 2A and 2B. Muscle Nerve 58:550–558PubMed

28.

Fischmann A, Hafner P, Gloor M et al (2013) Quantitative MRI and loss of free ambulation in Duchenne muscular dystrophy. J Neurol 260:969–974PubMed

29.

Fischer D, Hafner P, Rubino D et al (2016) The 6-minute walk test, motor function measure and quantitative thigh muscle MRI in Becker muscular dystrophy: a cross-sectional study. Neuromuscul Disord 26:414–422PubMed

30.

Yao L, Yip AL, Shrader JA et al (2016) Magnetic resonance measurement of muscle T₂, fat-corrected T₂ and fat fraction in the assessment of idiopathic inflammatory myopathies. Rheumatology (Oxford) 55:441–449

31.

Cohen J (1988) Statistical power analysis for the behavioral sciences. L. Erlbaum Associates, Hillsdale

32.

Middel B, Van Sonderen E (2002) Statistical significant change versus relevant or important change in (quasi) experimental design: some conceptual and methodological problems in estimating magnitude of intervention-related change in health services research. Int J Integr Care 2:e15PubMedPubMedCentral

33.

Azzabou N, de Sousa PL, Araujo EC, Carlier PG (2015) Validation of a generic approach to muscle water T₂ determination at 3T in fat-infiltrated skeletal muscle. J Magn Reson Imaging 41:645–653PubMed

34.

Chan BY, Gill KG, Rebsamen SL, Nguyen JC (2016) MR imaging of pediatric bone marrow. Radiographics 36:1911–1930PubMed

35.

Hooijmans MT, Niks EH, Burakiewicz J (2017) Non-uniform muscle fat replacement along the proximodistal axis in Duchenne muscular dystrophy. Neuromuscul Disord 27:458–464PubMed

36.

Reyngoudt H, Lopez Kolkovsky AL, Carlier PG (2019) Free intramuscular Mg²⁺ concentration calculated using both ³¹P and ¹H NMRS-based pH in skeletal muscle of Duchenne muscular dystrophy patients. NMR Biomed 32:e4115PubMed

37.

Willcocks RJ, Rooney WD, Triplett WT et al (2016) Multicenter prospective longitudinal study of magnetic resonance biomarkers in a large Duchenne muscular dystrophy cohort. Ann Neurol 79:535–547PubMedPubMedCentral

38.

Addison O, Marcus RL, Lastayo PC, Ryan AS (2014) Intermuscular fat: a review of the consequences and causes. Int J Endocrinol 2014:1–11

39.

Pons C, Borotikar B, Garetier M et al (2018) Quantifying skeletal muscle volume and shape in humans using MRI: a systematic review of validity and reliability. PLoS One 13:1–26

40.

Carlier PG, Shukelovich A, Baudin P-Y, Boisserie J-M, Le Louër J, Azzabou N (2014) Fast, precise, interactive segmentation of skeletal muscle NMR images. Neuromuscul Disord 24:836–837

41.

Baudin P-Y, Beyeler M, Carlier PG, Scheidegger O (2017) Interactive segmentation of leg muscles in NMR images. J Neuromuscul Dis 27:S126

42.

Snezhko E, Azzabou N, Baudin PY, Carlier PG (2018) Convolutional neural network segmentation of skeletal muscle NMR images Proc 27th ISMRM 2800

43.

Ghosh S, Ray N, Boulanger P (2017) A structured deep-learning based approach for the automated segmentation of human leg muscle from 3D MRI. Proc IEEE Int Conf Comput Vis 2017:117–123

44.

Mesbah S, Shalaby AM, Stills S et al (2019) Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals. PLoS One 14:e0216487PubMedPubMedCentral

45.

Amer R, Nassar J, Bendahan D, Greenspan H, Ben-Eliezer N (2019) Automatic segmentation of muscle tissue and inter-muscular fat in thigh and calf MRI images. Med Image Comput Comput Assist Interv 11765:219–227

46.

Li H, Luo H, Liu Y (2020) Paraspinal muscle segmentation based on deep neural network. Sensors (Basel) 19:e2650

47.

Verdu-Diaz J, Alonso-Perez J, Nunez-Peralta C et al (2020) Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies. Neurology 94:e1094–e1102PubMed

Titel: Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases
verfasst von: Harmen Reyngoudt
Benjamin Marty
Jean-Marc Boisserie
Julien Le Louër
Cedi Koumako
Pierre-Yves Baudin
Brenda Wong
Tanya Stojkovic
Anthony Béhin
Teresa Gidaro
Yves Allenbach
Olivier Benveniste
Laurent Servais
Pierre G. Carlier
Publikationsdatum: 21.11.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07487-0

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Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases