Advertisement

European Radiology

, Volume 28, Issue 7, pp 2872–2873 | Cite as

Editorial comment: the future of compositional MRI for cartilage

  • Thomas M. LinkEmail author
Musculoskeletal

Abstract

This editorial comment refers to the article: “Detection of early cartilage damage: feasibility and potential of gagCEST imaging at 7T” by Brinkhof et al, Eur Radiol 2018.

MRI-based cartilage compositional biomarkers, where do we stand?

In 1997, Dardzinski et al. published one of the first studies quantifying cartilage T2 in young asymptomatic volunteers [1], thus establishing the concept of compositional cartilage imaging. Over the next 20 years, new MRI-based techniques were developed, and the techniques were validated and investigated in clinical research studies. To date, the best established compositional imaging biomarkers are T2 and T1rho relaxation time mappings. It has been shown that compositional biomarkers can assess the mechanical properties of cartilage [2], predict focal cartilage breakdown [3] and also provide a risk assessment concerning the development of radiographic OA [4, 5].

In addition to T1rho and T2, other compositional biomarkers have been developed, which include delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), T2* imaging, sodium imaging, glycosaminoglycan chemical exchange saturation transfer imaging (gagCEST), diffusion-weighted imaging and diffusion tensor imaging. Some of these candidates, however, are unlikely to be used clinically, such as dGEMRIC (there was a recent Federal Food and Drug Administration warning concerning gadolinium storage in the body and brain for months to years) and sodium imaging (dedicated coils are required, and there is a low signal-to-noise ratio).

What is special about gagCEST?

Currently, one of the most promising new cartilage imaging biomarker candidates is gagCEST imaging, which allows mapping the glycosaminoglycan (GAG) concentration in cartilage. The first feasibility study was published in 2008 by Ling et al. [6] and showed that by exploiting the exchangeable protons of GAG it was possible to measure localized GAG concentrations in bovine patella samples. Subsequently, the technique has been used in a number of studies [7, 8, 9], mostly at 7T. Concerns were raised about using the technology at 3 T, and in their experimental study Singh et al. concluded that gagCEST is not expected to lead to accurate quantification of GAG content in healthy or degenerated cartilage at 3 T [9]. However, the investigators conceded that the technique holds promise as a clinically viable technique at 7T.

What does the current study tell us about gagCEST?

In this issue of European Radiology, Brinkhof et al. use gagCEST at 7T in volunteers and patients before cartilage repair surgery to show its clinical feasibility [10]. This study is also a first step to establishing this technique as an imaging biomarker. Required criteria for biomarkers include reproducibility, validity and the ability to assess disease burden and differentiate patients with and without disease, predict risk for disease and monitor therapy. The investigators developed a fast 3D gagCEST sequence and demonstrated its reproducibility, scanning each volunteer two times. They found excellent reproducibility with coefficients of variation ranging from 2.25% at the lateral condyle to 1.40% at the trochlea. They also validated the measurements using findings during cartilage repair surgery as a standard of reference and found a significantly different GAG effect in damaged cartilage compared with healthy cartilage at the contralateral condyle.

Where do we go from here?

gagCEST imaging is an exciting novel technique to better characterize localized GAG concentrations in cartilage. This study showed in a relatively small sample of subjects that the 3D gagCEST sequence developed by the investigators is reproducible and can show differences in GAG content between areas with advanced defects and healthy cartilage. While this is a promising first step, we have to consider the challenges and limitations: (1) Imaging at 7T is unlikely to become a standard clinical tool in the near future, and if this technology cannot eventually be developed for 3 T, it will likely not be feasible for larger patient populations, (2) a cartilage compositional biomarker needs to identify early changes of the cartilage matrix before focal defects occur, and using advanced International Cartilage Repair Society grades 3 and 4 lesions for validation is not a suitable standard of reference, (3) biomarkers need to be comparable between different machines and vendors, and (4) we need information on whether it can predict cartilage loss and monitor therapy. All these issuees will have to be addressed step-by-step and painstakingly before gagCEST has a future in compositional cartilage imaging. While the technique is clearly promising and the presented data are encouraging, the road ahead is steep and stony.

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Thomas M. Link

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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because this is an editorial without any study subjects.

Ethical approval

Institutional Review Board approval was not required because this is an editorial without any study subjects.

References

  1. 1.
    Dardzinski BJ, Mosher TJ, Li S, Van Slyke MA, Smith MB (1997) Spatial variation of T2 in human articular cartilage. Radiology 205(2):546–550CrossRefPubMedGoogle Scholar
  2. 2.
    Lammentausta E, Kiviranta P, Nissi MJ et al (2006) T2 relaxation time and delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of human patellar cartilage at 1.5 T and 9.4 T: Relationships with tissue mechanical properties. J Orthop Res. 24(3):366–374Google Scholar
  3. 3.
    Prasad AP, Nardo L, Schooler J, Joseph GB, Link TM (2013) T(1)rho and T(2) relaxation times predict progression of knee osteoarthritis. Osteoarthritis Cartilage. 21(1):69–76CrossRefPubMedGoogle Scholar
  4. 4.
    Joseph GB, McCulloch CE, Nevitt MC et al (2017) Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI: data from the osteoarthritis initiative. J Magn Reson ImagingGoogle Scholar
  5. 5.
    Liebl H, Joseph G, Nevitt MC et al (2015) Early T2 changes predict onset of radiographic knee osteoarthritis: data from the osteoarthritis initiative. Ann Rheum Dis. 74(7):1353–1359Google Scholar
  6. 6.
    Ling W, Regatte RR, Navon G, Jerschow A (2008) Assessment of glycosaminoglycan concentration in vivo by chemical exchange-dependent saturation transfer (gagCEST). Proc Natl Acad Sci U S A. 105(7):2266–2270CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Krishnamoorthy G, Nanga RPR, Bagga P, Hariharan H, Reddy R (2017) High quality three-dimensional gagCEST imaging of in vivo human knee cartilage at 7 Tesla. Magn Reson Med. 77(5):1866–1873CrossRefPubMedGoogle Scholar
  8. 8.
    Schmitt B, Zbyn S, Stelzeneder D et al (2011) Cartilage quality assessment by using glycosaminoglycan chemical exchange saturation transfer and (23)Na MR imaging at 7T. Radiology. 260(1):257–264Google Scholar
  9. 9.
    Singh A, Haris M, Cai K et al (2012) Chemical exchange saturation transfer magnetic resonance imaging of human knee cartilage at 3 T and 7T. Magn Reson Med. 68(2):588–594Google Scholar
  10. 10.
    Brinkhof S, Nizak R, Khlebnikov V, Prompers JJ, Klomp DWJ, Saris DBF (2018) Detection of early cartilage damage: feasibility and potential of gagCEST imaging at 7T. Eur RadiolGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology of Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations