CT-and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone

Federica Kiyomi Ciliberti, Lorena Guerrini, Arnar Evgeni Gunnarsson, Marco Recenti, Deborah Jacob, Vincenzo Cangiano, Yonatan Afework Tesfahunegn, Anna Sigríur Islind, Francesco Tortorella, Mariella Tsirilaki, Halldór Jónsson, Paolo Gargiulo*, Romain Aubonnet

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Original languageEnglish
Article number279
Issue number2
Publication statusPublished - Feb 2022

Bibliographical note

Funding Information:
Funding: This study is part of the European funded project RESTORE (https://restoreproject.eu/, accessed on 20 January 2022) (CORDIS grant agreement ID: 814558).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Other keywords

  • 3D modeling
  • Image segmentation
  • Knee joint
  • Machine learning
  • Medical imaging


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