European Radiology

, Volume 27, Issue 12, pp 4903–4912 | Cite as

Contrast-enhanced 3T MR Perfusion of Musculoskeletal Tumours: T1 Value Heterogeneity Assessment and Evaluation of the Influence of T1 Estimation Methods on Quantitative Parameters

  • Pedro Augusto Gondim Teixeira
  • Christophe Leplat
  • Bailiang Chen
  • Jacques De Verbizier
  • Marine Beaumont
  • Sammy Badr
  • Anne Cotten
  • Alain Blum



To evaluate intra-tumour and striated muscle T1 value heterogeneity and the influence of different methods of T1 estimation on the variability of quantitative perfusion parameters.

Material and methods

Eighty-two patients with a histologically confirmed musculoskeletal tumour were prospectively included in this study and, with ethics committee approval, underwent contrast-enhanced MR perfusion and T1 mapping. T1 value variations in viable tumour areas and in normal-appearing striated muscle were assessed. In 20 cases, normal muscle perfusion parameters were calculated using three different methods: signal based and gadolinium concentration based on fixed and variable T1 values.


Tumour and normal muscle T1 values were significantly different (p = 0.0008). T1 value heterogeneity was higher in tumours than in normal muscle (variation of 19.8% versus 13%). The T1 estimation method had a considerable influence on the variability of perfusion parameters. Fixed T1 values yielded higher coefficients of variation than variable T1 values (mean 109.6 ± 41.8% and 58.3 ± 14.1% respectively). Area under the curve was the least variable parameter (36%).


T1 values in musculoskeletal tumours are significantly different and more heterogeneous than normal muscle. Patient-specific T1 estimation is needed for direct inter-patient comparison of perfusion parameters.

Key Points

• T1 value variation in musculoskeletal tumours is considerable.

• T1 values in muscle and tumours are significantly different.

• Patient-specific T1 estimation is needed for comparison of inter-patient perfusion parameters.

• Technical variation is higher in permeability than semiquantitative perfusion parameters.


Neoplasms Striated muscle Musculoskeletal system Perfusion magnetic resonance imaging Quantitative analysis 



region of interest


time to 63% longitudinal magnetisation recovery


weighted fast spin-echo


number of excitations


field of view


high power field


spoiled gradient-echo


arterial input function


intraclass correlation coefficients


coefficient of variation


extravascular extracellular space


signal enhancement ratio


area under the curve

Max slope

maximum slope of increase


transfer constant from the plasma to the extravascular extracellular space


backflux constant


plasma volume


extravascular extracellular space volume


pigmented villonodular synovitis


giant cell tumour


Compliance with ethical standards


The scientific guarantor of this publication is Prof. Alain Blum.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.


This study has received funding by the Société française de radiologie (SFR) and the Collège des enseignants de Radiologie de France (CERF).

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• observational

• performed at one institution


  1. 1.
    Fisher SM, Joodi R, Madhuranthakam AJ et al (2016) Current utilities of imaging in grading musculoskeletal soft tissue sarcomas. Eur J Radiol 85:1336–1344CrossRefPubMedGoogle Scholar
  2. 2.
    Fayad LM, Jacobs MA, Wang X et al (2012) Musculoskeletal tumours: how to use anatomic, functional, and metabolic MR techniques. Radiology 265:340–356CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Griffith JF, Yeung DKW, Leung JCS et al (2011) Prediction of bone loss in elderly female subjects by MR perfusion imaging and spectroscopy. Eur Radiol 21:1160–1169CrossRefPubMedGoogle Scholar
  4. 4.
    Teixeira PAG, Chanson A, Beaumont M et al (2013) Dynamic MR imaging of osteoid osteomas: correlation of semiquantitative and quantitative perfusion parameters with patient symptoms and treatment outcome. Eur Radiol 23:2602–2611CrossRefPubMedGoogle Scholar
  5. 5.
    Heye T, Davenport MS, Horvath JJ et al (2013) Reproducibility of dynamic contrast-enhanced MR imaging Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology 266:801–811CrossRefPubMedGoogle Scholar
  6. 6.
    Beuzit L, Eliat P-A, Brun V et al (2015) Dynamic contrast-enhanced MRI: Study of inter-software accuracy and reproducibility using simulated and clinical data. J Magn Reson Imaging. doi: 10.1002/jmri.25101 PubMedGoogle Scholar
  7. 7.
    Cheng H-LM, Stikov N, Ghugre NR, Wright GA (2012) Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging 36:805–824CrossRefPubMedGoogle Scholar
  8. 8.
    Ivanidze J, Kallas ON, Gupta A et al (2016) Application of blood–brain barrier permeability imaging in global cerebral edema. AJNR Am J Neuroradiol. doi: 10.3174/ajnr.A4784 Google Scholar
  9. 9.
    Brix G, Semmler W, Port R et al (1991) Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr 15:621–628CrossRefPubMedGoogle Scholar
  10. 10.
    Hittmair K, Gomiscek G, Langenberger K et al (1994) Method for the quantitative assessment of contrast agent uptake in dynamic contrast-enhanced MRI. Magn Reson Med 31:567–571CrossRefPubMedGoogle Scholar
  11. 11.
    Aronhime S, Calcagno C, Jajamovich G et al (2014) DCE-MRI of the liver: effect of linear and non linear conversions on hepatic perfusion quantification and reproducibility. J Magn Reson Imaging 40:90–98CrossRefPubMedGoogle Scholar
  12. 12.
    Stikov N, Boudreau M, Levesque IR et al (2015) On the accuracy of T1 mapping: searching for common ground. Magn Reson Med 73:514–522CrossRefPubMedGoogle Scholar
  13. 13.
    Piechnik SK, Ferreira VM, Lewandowski AJ et al (2013) Normal variation of magnetic resonance T1 relaxation times in the human population at 1.5 T using ShMOLLI. J Cardiovasc Magn Reson 15:13CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Pineda FD, Medved M, Fan X, Karczmar GS (2016) B1 and T1 mapping of the breast with a reference tissue method. Magn Reson Med 75:1565–1573CrossRefPubMedGoogle Scholar
  15. 15.
    O’Connor JPB, Rose CJ, Waterton JC et al (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257CrossRefPubMedGoogle Scholar
  16. 16.
    Trojani M, Contesso G, Coindre JM et al (1984) Soft-tissue sarcomas of adults; study of pathological prognostic variables and definition of a histopathological grading system. Int J Cancer 33:37–42CrossRefPubMedGoogle Scholar
  17. 17.
    Abdul-Karim FW, Bauer TW, Kilpatrick SE et al (2004) Recommendations for the reporting of bone tumors. Association of Directors of Anatomic and Surgical Pathology. Hum Pathol 35:1173–1178CrossRefPubMedGoogle Scholar
  18. 18.
    Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232CrossRefPubMedGoogle Scholar
  19. 19.
    Dyke JP, Panicek DM, Healey JH et al (2003) Osteogenic and Ewing sarcomas: estimation of necrotic fraction during induction chemotherapy with dynamic contrast-enhanced MR imaging. Radiology 228:271–278CrossRefPubMedGoogle Scholar
  20. 20.
    Leach MO, Brindle KM, Evelhoch JL et al (2005) The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 92:1599–1610CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Schoierer O, Bloess K, Bender D et al (2014) Dynamic contrast-enhanced magnetic resonance imaging can assess vascularity within fracture non-unions and predicts good outcome. Eur Radiol 24:449–459CrossRefPubMedGoogle Scholar
  22. 22.
    Gondim Teixeira PA, Gay F, Chen B et al (2016) Diffusion-weighted magnetic resonance imaging for the initial characterization of non-fatty soft tissue tumors: correlation between T2 signal intensity and ADC values. Skelet Radiol 45:263–271CrossRefGoogle Scholar
  23. 23.
    Crawley AP, Henkelman RM (1988) A comparison of one-shot and recovery methods in T1 imaging. Magn Reson Med 7:23–34CrossRefPubMedGoogle Scholar
  24. 24.
    Cheng H-LM, Wright GA (2006) Rapid high-resolution T(1) mapping by variable flip angles: accurate and precise measurements in the presence of radiofrequency field inhomogeneity. Magn Reson Med 55:566–574CrossRefPubMedGoogle Scholar
  25. 25.
    Hawighorst H, Libicher M, Knopp MV et al (1999) Evaluation of angiogenesis and perfusion of bone marrow lesions: role of semiquantitative and quantitative dynamic MRI. J Magn Reson Imaging 10:286–294CrossRefPubMedGoogle Scholar
  26. 26.
    Ganeshan B, Miles KA, Babikir S et al (2016) CT-based texture analysis potentially provides prognostic information complementary to interim FDG-PET for patients with Hodgkin’s and aggressive non-Hodgkin’s lymphomas. Eur Radiol. doi: 10.1007/s00330-016-4470-8 Google Scholar
  27. 27.
    Giganti F, Antunes S, Salerno A et al (2016) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. doi: 10.1007/s00330-016-4540-y PubMedGoogle Scholar
  28. 28.
    Sidhu HS, Benigno S, Ganeshan B et al (2016) Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur Radiol. doi: 10.1007/s00330-016-4579-9 PubMedPubMedCentralGoogle Scholar
  29. 29.
    Kim JH, Ko ES, Lim Y, et al. (2016) Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology 160261. doi:  10.1148/radiol.2016160261
  30. 30.
    Foroutan P, Kreahling JM, Morse DL et al (2013) Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy. PLoS ONE 8, e82875. doi: 10.1371/journal.pone.0082875 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Verma RK, Slotboom J, Locher C et al (2016) Characterization of enhancing MS lesions by dynamic texture parameter analysis of dynamic susceptibility perfusion imaging. Biomed Res Int 2016:9578139CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Pedro Augusto Gondim Teixeira
    • 1
  • Christophe Leplat
    • 1
  • Bailiang Chen
    • 2
  • Jacques De Verbizier
    • 1
  • Marine Beaumont
    • 2
  • Sammy Badr
    • 3
  • Anne Cotten
    • 3
  • Alain Blum
    • 1
  1. 1.Service D’imagerie GuillozHôpital Central, CHRU-NancyNancy cedexFrance
  2. 2.Université de Lorraine, laboratoire IADI, UMR S 947NancyFrance
  3. 3.Department of Radiology and Musculoskeletal ImagingCHRU Lille Centre de Consultations et d’Imagerie de l’Appareil LocomoteurLilleFrance

Personalised recommendations