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Article

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

1
Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland
2
Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, 84084 Salerno, Italy
3
Laboratory of Cellular and Molecular Engineering “Silvio Cavalcanti”, Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, 47521 Cesena, Italy
4
Department of Engineering, Reykjavik University, 101 Reykjavik, Iceland
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Department of Computer Science, Reykjavik University, 101 Reykjavik, Iceland
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Department of Radiology, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland
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Department of Orthopaedics, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland
8
Medical Faculty, University of Iceland, 101 Reykjavik, Iceland
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Department of Science, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Usha Sinha and Ugo Carraro
Diagnostics 2022, 12(2), 279; https://doi.org/10.3390/diagnostics12020279
Received: 7 December 2021 / Revised: 10 January 2022 / Accepted: 20 January 2022 / Published: 22 January 2022
(This article belongs to the Special Issue Translational Research on Muscle and Mobility Medicine)
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. View Full-Text
Keywords: knee joint; medical imaging; image segmentation; machine learning; 3D modeling knee joint; medical imaging; image segmentation; machine learning; 3D modeling
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MDPI and ACS Style

Ciliberti, F.K.; Guerrini, L.; Gunnarsson, A.E.; Recenti, M.; Jacob, D.; Cangiano, V.; Tesfahunegn, Y.A.; Islind, A.S.; Tortorella, F.; Tsirilaki, M.; Jónsson, H., Jr.; Gargiulo, P.; Aubonnet, R. CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone. Diagnostics 2022, 12, 279. https://doi.org/10.3390/diagnostics12020279

AMA Style

Ciliberti FK, Guerrini L, Gunnarsson AE, Recenti M, Jacob D, Cangiano V, Tesfahunegn YA, Islind AS, Tortorella F, Tsirilaki M, Jónsson H Jr., Gargiulo P, Aubonnet R. CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone. Diagnostics. 2022; 12(2):279. https://doi.org/10.3390/diagnostics12020279

Chicago/Turabian Style

Ciliberti, Federica K., Lorena Guerrini, Arnar E. Gunnarsson, Marco Recenti, Deborah Jacob, Vincenzo Cangiano, Yonatan A. Tesfahunegn, Anna S. Islind, Francesco Tortorella, Mariella Tsirilaki, Halldór Jónsson Jr., Paolo Gargiulo, and Romain Aubonnet. 2022. "CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone" Diagnostics 12, no. 2: 279. https://doi.org/10.3390/diagnostics12020279

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