TY - JOUR
T1 - CT-and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone
AU - Ciliberti, Federica Kiyomi
AU - Guerrini, Lorena
AU - Gunnarsson, Arnar Evgeni
AU - Recenti, Marco
AU - Jacob, Deborah
AU - Cangiano, Vincenzo
AU - Tesfahunegn, Yonatan Afework
AU - Islind, Anna Sigríur
AU - Tortorella, Francesco
AU - Tsirilaki, Mariella
AU - Jónsson, Halldór
AU - Gargiulo, Paolo
AU - Aubonnet, Romain
N1 - 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.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - 3D modeling
KW - Image segmentation
KW - Knee joint
KW - Machine learning
KW - Medical imaging
UR - http://www.scopus.com/inward/record.url?scp=85123179411&partnerID=8YFLogxK
U2 - 10.3390/diagnostics12020279
DO - 10.3390/diagnostics12020279
M3 - Article
C2 - 35204370
AN - SCOPUS:85123179411
SN - 2075-4418
VL - 12
JO - Diagnostics
JF - Diagnostics
IS - 2
M1 - 279
ER -