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Aubonnet R, Ramos J, Recenti M, Jacob D, Ciliberti F, Guerrini L, Gislason MK, Sigurjonsson O, Tsirilaki M, Jónsson H, Gargiulo P. Toward New Assessment of Knee Cartilage Degeneration. Cartilage 2023; 14:351-374. [PMID: 36541701 PMCID: PMC10601563 DOI: 10.1177/19476035221144746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/09/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
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 sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. CONCLUSION The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.
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Affiliation(s)
- Romain Aubonnet
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Jorgelina Ramos
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Marco Recenti
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Deborah Jacob
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Federica Ciliberti
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Lorena Guerrini
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Magnus K. Gislason
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | - Olafur Sigurjonsson
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
| | | | - Halldór Jónsson
- Landspitali, University Hospital of Iceland, Reykjavik, Iceland
- Medical Faculty, University of Iceland, Reykjavik, Iceland
| | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
- Landspitali, University Hospital of Iceland, Reykjavik, Iceland
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Ciliberti FK, Guerrini L, Gunnarsson AE, Recenti M, Jacob D, Cangiano V, Tesfahunegn YA, Islind AS, Tortorella F, Tsirilaki M, Jónsson H, Gargiulo P, Aubonnet R. CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone. Diagnostics (Basel) 2022; 12:279. [PMID: 35204370 PMCID: PMC8870751 DOI: 10.3390/diagnostics12020279] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/10/2022] [Accepted: 01/20/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- Federica Kiyomi Ciliberti
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
- Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, 84084 Salerno, Italy;
| | - Lorena Guerrini
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
- Laboratory of Cellular and Molecular Engineering “Silvio Cavalcanti”, Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, 47521 Cesena, Italy
| | - Arnar Evgeni Gunnarsson
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
| | - Marco Recenti
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
| | - Deborah Jacob
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
| | - Vincenzo Cangiano
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
| | | | | | - Francesco Tortorella
- Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, 84084 Salerno, Italy;
| | - Mariella Tsirilaki
- Department of Radiology, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland;
| | - Halldór Jónsson
- Department of Orthopaedics, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland;
- Medical Faculty, University of Iceland, 101 Reykjavik, Iceland
| | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
- Department of Science, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland
| | - Romain Aubonnet
- Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland; (F.K.C.); (L.G.); (A.E.G.); (M.R.); (D.J.); (V.C.); (R.A.)
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Gandhamal A, Talbar S, Gajre S, Hani AFM, Kumar D. Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images. Comput Biol Med 2017; 83:120-133. [DOI: 10.1016/j.compbiomed.2017.03.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 02/09/2017] [Accepted: 03/01/2017] [Indexed: 10/20/2022]
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Non-invasive and in vivo assessment of osteoarthritic articular cartilage: a review on MRI investigations. Rheumatol Int 2014; 35:1-16. [PMID: 24879325 DOI: 10.1007/s00296-014-3052-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 05/16/2014] [Indexed: 10/25/2022]
Abstract
Early detection of knee osteoarthritis (OA) is of great interest to orthopaedic surgeons, rheumatologists, radiologists, and researchers because it would allow physicians to provide patients with treatments and advice to slow the onset or progression of the disease. Early detection can be achieved by identifying early changes in selected features of degenerative articular cartilage (AC) using non-invasive imaging modalities. Magnetic resonance imaging (MRI) is becoming the standard for assessment of OA. The aim of this paper was to review the influence of MRI on the selection, detection, and measurement of AC features associated with early OA. Our review of the literature indicates that the changes associated with early OA are in cartilage thickness, cartilage volume, cartilage water content, and proteoglycan content that can be accurately, consistently, and non-invasively measured using MRI. Choosing an MR pulse sequence that provides the capability to assess cartilage physiology and morphology in a single acquisition and advanced multi-nuclei MRI is desirable. The results of the review indicate that using an ultra-high magnetic strength, MR imager does not affect early OA detection. In conclusion, MRI is currently the most suitable modality for early detection of knee OA, and future research should focus on the quantitative evaluation of early OA features using advances in MR hardware, software, and data processing with sophisticated image/pattern recognition techniques.
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