Toward New Assessment of Knee Cartilage Degeneration

• Published in: CARTILAGE
• Main Authors: Aubonnet, Romain, Ramos, Jorgelina, Recenti, Marco, Jacob, Deborah, Ciliberti, Federica, Guerrini, Lorena, Gislason, Magnus K., Sigurjonsson, Olafur, Tsirilaki, Mariella, Jónsson, Halldór, Gargiulo, Paolo
• Other Authors: Department of Engineering, Clinical Laboratory Services, Diagnostics and Blood Bank, Faculty of Medicine, Surgical Services, Other departments
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Myndgreining (læknisfræði); Bæklunarskurðlæknisfræði; Verkfræðingar; 3D models; classification; image segmentation; knee cartilage; osteoarthritis; Medicine (all); Örn; Iceland
• Online Access: https://hdl.handle.net/20.500.11815/4422; https://doi.org/10.1177/19476035221144746

Funding Information: The authors would like to thank the project RESTORE for their contribution to this study, Marco Ghiselli and Kristján Örn Jóhannesson from the National University Hospital of Iceland for the medical image acquisition, Vicenzo Cangiano for his help in medical image segmentation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is part of the European project RESTORE ( https://restoreproject.eu/ ), funded by the European Union’s Horizon 2020 research and innovation program (grant agreement ID: 814558). This work has also been funded by Landspitalin Science fund (grant number: 960221). Publisher Copyright: © The Author(s) 2022. Publisher Copyright: © The Author(s) 2022. Objective: Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. Design: We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. Results: Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest ...