A quantitative alternative to the Goutallier classification system using Lava Flex and Ideal MRI techniques: volumetric intramuscular fatty infiltration of the supraspinatus muscle, a cadaveric study

  • Jose H. TrevinoIII
  • Krzysztof R. Gorny
  • Angel Gomez-Cintron
  • Chunfeng Zhao
  • Hugo GiambiniEmail author
Short Communication



The Goutallier classification system is the most commonly used method for grading intramuscular fatty infiltration in rotator cuff tears. This grading system presents low inter-observer reliability and an inability to provide quantitative and repeatable outcomes for intramuscular fat. We determined the correlation and reliability of two methods, the Lava Flex and Ideal IQ MRI techniques, in quantifying volumetric intramuscular fat, while also comparing to the Goutallier method.

Materials and methods

The supraspinatus muscles of seventeen cadaveric shoulders were scanned using the Lava Flex and Ideal IQ MRI imaging protocols. Histological analysis was performed on the same muscles. Agreement, reliability, and correlation analyses were performed to compare all outcomes.


The Lava Flex protocol took an average of ~ 4 min, while the Ideal IQ required about ~ 11 min to complete. Bland–Altman analysis showed good agreement between the Lava Flex and Ideal IQ [LOA (− 0.10 and 0.05)], and ICC analyses showed excellent reliability (ICC (1,1) 0.948; ICC (2,1) 0.947). There was a 91% correlation between the Lava Flex and Ideal IQ MR protocols. Weighted Kappa analysis between histology and the Goutallier classification showed fair-to-moderate agreement.


The Lava Flex technique, taking about 30% of the acquisition time, may prevent motion artifacts in outcomes associated with the longer Ideal IQ technique. However, potential magnetic field inhomogeneities should be considered. The Lava Flex technique may be a faster and valid alternative to the Goutallier classification system.


Magnetic resonance imaging Muscle properties Rotator cuff tear Fat infiltration Goutallier classification 



The study was funded internally by the University of Texas at San Antonio and an award from the Mayo Clinic.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest that may have influenced or imparted bias on the work.

Ethical standard

Cadaveric shoulders used in this study were obtained after institutional review board approval.


  1. 1.
    Fehringer EV, Sun JF, VanOeveren LS, Keller BK, Matsen FA (2008) Full-thickness rotator cuff tear prevalence and correlation with function and co-morbidities in patients sixty-five years and older. J Shoulder Elbow Surg 17(6):881–885CrossRefGoogle Scholar
  2. 2.
    McElvany MD, McGoldrick E, Gee AO, Neradilek MB, Matsen FA (2015) Rotator cuff repair published evidence on factors associated with repair integrity and clinical outcome. Am J Sports Med 43(2):491–500CrossRefGoogle Scholar
  3. 3.
    Matsen FA (2008) Rotator-cuff failure. N Engl J Med 358:20CrossRefGoogle Scholar
  4. 4.
    Gladstone JN, Bishop JY, Lo IKY, Flatow EL (2007) Fatty infiltration and atrophy of the rotator cuff do not improve after rotator cuff repair and correlate with poor functional outcome. Am J Sports Med 35(5):719–728CrossRefGoogle Scholar
  5. 5.
    Ashry R, Schweitzer ME, Cunningham P, Cohen J, Babb J, Cantos A (2007) Muscle atrophy as a consequence of rotator cuff tears: should we compare the muscles of the rotator cuff with those of the deltoid? Skeletal Radiol 36(9):841–845CrossRefGoogle Scholar
  6. 6.
    Goutallier D, Postel JM, Bernageau J, Lavau L, Voisin MC (1994) Fatty muscle degeneration in cuff ruptures Pre- and postoperative evaluation by CT scan. Clin Orthop Relat Res 304:78–83Google Scholar
  7. 7.
    Davis DL, Kesler T, Gilotra MN, Almardawi R, Hasan SA, Gullapalli RP et al (2018) Quantification of shoulder muscle intramuscular fatty infiltration on T1-weighted MRI: a viable alternative to the Goutallier classification system. Skeletal Radiol 48:535–541CrossRefGoogle Scholar
  8. 8.
    Gilbert F, Bohm D, Eden L, Schmalzl J, Meffert RH, Kostler H et al (2016) Comparing the MRI-based Goutallier Classification to an experimental quantitative MR spectroscopic fat measurement of the supraspinatus muscle. BMC Musculoskelet Disord 17(1):355CrossRefGoogle Scholar
  9. 9.
    Lippe J, Spang JT, Leger RR, Arciero RA, Mazzocca AD, Shea KP (2012) Inter-rater agreement of the goutallier, patte, and warner classification scores using preoperative magnetic resonance imaging in patients with rotator cuff tears. Arthroscopy J Arthroscopic Relat Surg 28(2):154–159CrossRefGoogle Scholar
  10. 10.
    Fischer MA, Nanz D, Shimakawa A, Schirmer T, Guggenberger R, Chhabra A et al (2013) Quantification of muscle fat in patients with low back pain: comparison of multi-echo MR imaging with single-voxel MR spectroscopy. Radiology 266(2):555–563CrossRefGoogle Scholar
  11. 11.
    Lee S, Lucas RM, Lansdown DA, Nardo L, Lai A, Link TM et al (2015) Magnetic resonance rotator cuff fat fraction and its relationship with tendon tear severity and subject characteristics. J Shoulder Elbow Surg 24(9):1442–1451CrossRefGoogle Scholar
  12. 12.
    Giambini H, Hatta T, Gorny KR, Widholm P, Karlsson A, Leinhard OD et al (2018) Intramuscular fat infiltration evaluated by magnetic resonance imaging predicts the extensibility of the supraspinatus muscle. Muscle Nerve 57(1):129–135CrossRefGoogle Scholar
  13. 13.
    Rydell J, Knutsson H, Pettersson J, Johansson A, Farneback G, Dahlqvist O et al (2007) Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient. Med Image Comput Comput Assist Interv 10(Pt 1):210–218PubMedGoogle Scholar
  14. 14.
    Andersson T, Romu T, Karlsson A, Noren B, Forsgren MF, Smedby O et al (2015) Consistent intensity inhomogeneity correction in water-fat MRI. J Magn Reson Imaging 42(2):468–476CrossRefGoogle Scholar
  15. 15.
    Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128CrossRefGoogle Scholar
  16. 16.
    Kim HM, Galatz LM, Lim C, Havlioglu N, Thomopoulos S (2012) The effect of tear size and nerve injury on rotator cuff muscle fatty degeneration in a rodent animal model. J Shoulder Elbow Surg 21(7):847–858CrossRefGoogle Scholar
  17. 17.
    Post M, Silver R, Singh M (1983) Rotator cuff tear. Diagnosis and treatment. Clin Orthop Relat Res 173:78–91Google Scholar
  18. 18.
    Myles PS, Cui J (2007) Using the Bland-Altman method to measure agreement with repeated measures. Br J Anaesth 99(3):309–311CrossRefGoogle Scholar
  19. 19.
    Koo TK, Li MY (2017) A guideline of selecting and reporting intraclass correlation coefficients for reliability research (vol 15, pg 155, 2016). J Chiropractic Med 16(4):346–346CrossRefGoogle Scholar
  20. 20.
    McGraw KO, Wong SP (1996) Forming inferences about some intraclass correlation coefficients. Psychol Methods 1(1):30–46CrossRefGoogle Scholar
  21. 21.
    Yamamoto A, Takagishi K, Osawa T, Yanagawa T, Nakajima D, Shitara H et al (2010) Prevalence and risk factors of a rotator cuff tear in the general population. J Shoulder Elbow Surg 19(1):116–120CrossRefGoogle Scholar
  22. 22.
    Fucentese SF, von Roll AL, Pfirrmann CW, Gerber C, Jost B (2012) Evolution of nonoperatively treated symptomatic isolated full-thickness supraspinatus tears. J Bone Jt Surg Am Vol 94(9):801–808CrossRefGoogle Scholar
  23. 23.
    Melis B, Nemoz C, Walch G (2009) Muscle fatty infiltration in rotator cuff tears: descriptive analysis of 1688 cases. Orthop Traumatol Surg Res 95(5):319–324CrossRefGoogle Scholar
  24. 24.
    Goutallier D, Postel JM, Gleyze P, Leguilloux P, Van Driessche S (2003) Influence of cuff muscle fatty degeneration on anatomic and functional outcomes after simple suture of full-thickness tears. J Shoulder Elbow Surg 12(6):550–554CrossRefGoogle Scholar
  25. 25.
    Oh JH, Kim SH, Choi JA, Kim Y, Oh CH (2010) Reliability of the grading system for fatty degeneration of rotator cuff muscles. Clin Orthop Relat Res 468(6):1558–1564CrossRefGoogle Scholar
  26. 26.
    Spencer EE, Dunn WR, Wright RW, Wolf BR, Spindler KP, McCarty E et al (2008) Interobserver agreement in the classification of rotator cuff tears using magnetic resonance imaging. Am J Sports Med 36(1):99–103CrossRefGoogle Scholar
  27. 27.
    Colvin AC, Egorova N, Harrison AK, Moskowitz A, Flatow EL (2012) National trends in rotator cuff repair. J Bone Jt Surg Am 94(3):227–233CrossRefGoogle Scholar
  28. 28.
    Grimm A, Meyer H, Nickel MD, Nittka M, Raithel E, Chaudry O et al (2018) Evaluation of 2-point, 3-point, and 6-point Dixon magnetic resonance imaging with flexible echo timing for muscle fat quantification. Eur J Radiol 103:57–64CrossRefGoogle Scholar
  29. 29.
    Li XH, Zhu J, Zhang XM, Ji YF, Chen TW, Huang XH et al (2014) Abdominal MRI at 3.0 T: lava-flex compared with conventional fat suppression T1-weighted images. J Magn Reson Imaging 40(1):58–66CrossRefGoogle Scholar
  30. 30.
    Horiuchi S, Nozaki T, Tasaki A, Yamakawa A, Kaneko Y, Hara T et al (2017) Reliability of MR quantification of rotator cuff muscle fatty degeneration using a 2-point dixon technique in comparison with the Goutallier classification: validation study by multiple readers. Acad Radiol 24(11):1343–1351CrossRefGoogle Scholar
  31. 31.
    Santago AC 2nd, Vidt ME, Tuohy CJ, Poehling GG, Freehill MT, Jordan JH et al (2016) Quantitative analysis of three-dimensional distribution and clustering of intramuscular fat in muscles of the rotator cuff. Ann Biomed Eng 44(7):2158–2167CrossRefGoogle Scholar

Copyright information

© European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringThe University of Texas at San AntonioSan AntonioUSA
  2. 2.Department of RadiologyMayo ClinicRochesterUSA
  3. 3.Department of RadiologyThe University of Texas Health Science Center, San AntonioSan AntonioUSA
  4. 4.Department of Orthopedic SurgeryMayo ClinicRochesterUSA

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