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  • Review Article
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Advances in cartilage imaging techniques

Nature Reviews Rheumatology (2026)Cite this article

Subjects

Abstract

Articular cartilage is crucial for joint function; however, it has limited regenerative capacity when damaged, a hallmark of many rheumatic diseases. Non-invasive imaging is essential for early diagnosis, therapeutic monitoring and prognostication. MRI remains the reference standard, offering detailed assessment of both morphological and compositional cartilage changes. Technological advances, including high-resolution and compositional MRI techniques such as T2 mapping, T1ρ, delayed gadolinium-enhanced MRI of cartilage, sodium imaging, diffusion imaging and ultra-short echo-time imaging, enable early detection of matrix alterations that precede structural breakdown. CT arthrography, although it involves radiation, serves as a valuable alternative when MRI is contra-indicated, offering high performance in the detection and evaluation of cartilage surface lesions. Emerging modalities, such as ultrasonography and PET, offer additional functional insights but are currently limited in scope. Artificial intelligence is poised to transform cartilage imaging through accelerated acquisition, automated segmentation, improved interpretation and enhanced efficiency, with growing clinical adoption. Advanced cartilage imaging will probably have an increasingly important role in clinical rheumatology, particularly for the optimization of individualized management of cartilage pathology.

Key points

  • MRI is the primary imaging technique used to directly visualize articular cartilage. All MRI field strengths can be used for cartilage imaging, whereas higher magnetic field strength allows for greater spatial resolution and shorter acquisition times.

  • Semi-quantitative MRI assessment allows for evaluation of cartilage surface damage regarding area extent and depth, but also covers other articular structures, such as subchondral bone and menisci.

  • Quantitative cartilage morphometry involves cartilage tissue segmentation from MRI and computation of diverse 3D measures; this technique is highly sensitive to change over time.

  • Compositional MRI techniques allow for the evaluation of the cartilage ultrastructure prior to the presence of surface damage and can be used as biomarkers for early cartilage degeneration.

  • Advances in the CT field include the advent of spectral, photon-counting and weight-bearing CT, whereas the application of PET and other nuclear medicine techniques to cartilage has been limited.

  • Artificial intelligence is increasingly used in cartilage evaluation to accelerate MRI acquisition and improve image quality. Artificial intelligence-based automated segmentation enables precise cartilage quantification.

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Fig. 1: Direct comparison between 3 T and 7 T MRI.
Fig. 2: Relevance of sequence selection for evaluation of focal cartilage defects.
Fig. 3: Cartilage morphometry based on MRI.
Fig. 4: Direct comparison of different magnetic field strengths for cartilage surface damage evaluation.
Fig. 5: Comparison between CT arthrography and MRI.
Fig. 6: Metabolic imaging.

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Authors and Affiliations

  1. Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA

    Ali Guermazi

  2. Department of Radiology, VA Boston Healthcare System, West Roxbury, MA, USA

    Ali Guermazi & Frank W. Roemer

  3. Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology Paracelsus Medical University (PMU), Salzburg, Austria

    Felix Eckstein

  4. Chondrometrics GmbH, Freilassing, Germany

    Felix Eckstein & Wolfgang Wirth

  5. Department of Radiology, Stanford University, Stanford, CA, USA

    Garry Gold & Feliks Kogan

  6. Department of Radiology, Tufts University School of Medicine, Boston, MA, USA

    Daichi Hayashi

  7. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Mohamed Jarraya

  8. Program of Advanced Musculoskeletal Imaging, Departments of Biomedical Engineering and Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA

    Xiaojuan Li

  9. Department of Radiology, University of California at San Francisco, San Francisco, CA, USA

    Thomas M. Link

  10. Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

    Sven Nebelung

  11. Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands

    Edwin H. G. Oei

  12. Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Patrick Omoumi

  13. Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland

    Simo Saarakkala

  14. Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland

    Simo Saarakkala

  15. Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria

    Siegfried Trattnig

  16. Department of Radiology, Universitätsklinikum, Erlangen, Germany

    Frank W. Roemer

  17. Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

    Frank W. Roemer

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Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Ali Guermazi.

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Competing interests

A.G. is the president and co-owner of Boston Core Imaging Lab (BICL), LLC., a company that provides image assessment devices to academia and the pharmaceutical industry; has received consulting fees from TissueGene, Novartis, ICM, Paradigm, Formation Bio, 4Moving, Scarcell Therapeutics, Pacira, Coval, Medipost, Levicept and Peptinov; is the co-editor-in-chief of Skeletal Radiology. F.E. is the co-owner and CEO of Chondrometrics GmbH, a company that provides MR image analysis services to academic researchers and to the pharmaceutical industry; has provided consulting services to MerckSerono, Galapagos/Servier, Kolon Tissue Gene, Novartis, Peptinov, Formation Bio, 4 P Pharma, Sanofi and Artialis. D.H. has received publication royalties from Wolters Kluwer. S.N. has received lecture honoraria by Bayer Vital AG. E.H.G.O. has received research support from GE Healthcare. W.W. is in part-time employment with Chondrometrics GmbH and is a co-owner of Chondrometrics GmbH. F.W.R. is the CMO, director of research and shareholder of Boston Imaging Core Lab (BICL), LLC. F.W.R. is editor-in-chief of Osteoarthritis Imaging. G.G., M.J., F.K., X.L., T.M.L., P.O., S.S. and S.T. declare no competing interests.

Peer review

Peer review information

Nature Reviews Rheumatology thanks Ichiro Sekiya, Vladimir Juras and Mikko Nissi for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Review criteria

A non-systematic literature search was performed using PubMed, starting with a list of terms including the title of the current manuscript, followed by multiple search terms including but not limited to “imaging”, “cartilage”, “quantitative”, “semiquantitative”, “joint”, “whole organ”, “joint tissue”, “joint structure”, “assessment”, “score”, “measure”, “compositional MRI”, “computed tomography”, “ultrasound”, “metabolic imaging”, “PET”, “ultrashort echo time imaging”, “diffusion imaging”, “T2 Mapping”, “T1rho”, “delayed gadolinium enhanced MRI of cartilage”, “arthrography”, “reliability”, “artificial intelligence” and possible variants for each. The focus of the literature search was on articles published in the past 5 years. Results of the search were reviewed by the first and last authors to determine the relevance of the selected publications for the focus topic of this manuscript. All authors of this article made a joint decision on which articles to include and considered the most relevant. Finally, to receive broad and detailed input, informal (that is, non-structured) consultations were held with domain experts in various research areas. The identified methodologies, findings, concepts and recommendations were then organized into a systematic framework, providing an overview of advances in cartilage imaging, considering the state-of-the-art of current imaging science.

Supplementary information

Glossary

Arthrography

Technique that is used for direct visualization of cartilage surface damage after intra-articular injection of either an iodine-based (CT) or gadolinium-based (MRI) contrast agent.

Compositional MRI

Umbrella term for MRI techniques that allow for evaluation of the cartilage ultrastructure including but not limited to assessment of collagen content and organization, water content and glycosaminoglycan content.

Convolutional neural network

(CNN). Type of deep-learning model commonly used in medical imaging to automatically detect and analyse patterns in images, such as identifying tumours or segmenting organs.

Delayed gadolinium-enhanced MRI of cartilage

(dGEMRIC). Compositional MRI technique that involves intra-articular administration of a gadolinium-based contrast agent using repulsive forces between a negatively charged contrast agent (commonly gadopentetate dimeglumine) and negative charges on glycosaminoglycans to indirectly measure glycosaminoglycan content.

Dice similarity coefficient

(DSC). Statistical measure used to evaluate the spatial overlap between two segmented regions, such as an automated segmentation and a ground truth (manual segmentation). It ranges from 0 (no overlap) to 1 (perfect overlap) and is commonly used to assess the accuracy of tissue or lesion segmentation in medical imaging.

Diffusion imaging

Compositional MRI technique that enables visualization of the mobility of water molecules within a given tissue. Increased mobility reflects early cartilage pathology.

PET

Imaging technique that uses different radioactive tracers that are administered intra-venously to visualize and measure metabolic activity of a given tissue.

Segmentation-based quantitative MRI

Using high-resolution 3D MRI sequences that offer a high contrast between cartilage, the subchondral bone and intra-articular joint fluid, quantitative assessment is based on manual, automated or semi-automated segmentation of cartilage. Several parameters are being extracted, including thickness, volume, amount of full-thickness damage or joint surface area.

Semi-quantitative MRI assessment

Applying ordinal grading scales, semi-quantitative cartilage assessment is performed by expert readers considering area extent and depth of cartilage damage in a specific anatomical subregion of a joint.

T1ρ

Compositional MRI technique, which is also termed spin-lattice relaxation time in the rotating frame, reflects the proteoglycan concentration within the cartilage matrix by probing the interactions between motion-restricted water molecules and their local macromolecular environment.

T2 mapping

Compositional MRI technique that quantifies the T2 relaxation time of a given tissue. In the context of cartilage imaging, T2 reflects collagen content and organization and water content.

Ultra-short echo-time imaging

(UTE). Very low echo times at MRI acquisition enable visualization of tissues that do not commonly exhibit a signal such as bone or tendons or the deep calcified layer of cartilage.

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Guermazi, A., Eckstein, F., Gold, G. et al. Advances in cartilage imaging techniques. Nat Rev Rheumatol (2026). https://doi.org/10.1038/s41584-026-01353-x

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