Ciliberti Federica Kiyomi, Guerrini Lorena, Gunnarsson Arnar Evgeni, Recenti Marco, Jacob Deborah, Cangiano Vincenzo, Tesfahunegn Yonatan Afework, Islind Anna Sigríður, Tortorella Francesco, Tsirilaki Mariella, Jónsson Halldór, Gargiulo Paolo, Aubonnet Romain
Institute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.
Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, 84084 Salerno, Italy.
Diagnostics (Basel). 2022 Jan 22;12(2):279. doi: 10.3390/diagnostics12020279.
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.
对于人体关节软骨的观察,X射线、计算机断层扫描(CT)或磁共振成像(MRI)是评估病变或创伤的主要诊断工具。当前的工作基于膝关节的MRI和CT数据引入了一组新颖的测量方法和三维特征,用于重建骨骼和软骨,并从新的角度评估软骨状况。本研究招募了47名患有退行性疾病、创伤性损伤或无症状或无创伤的受试者,并使用CT和MRI进行扫描。利用医学成像软件,对膝关节的骨骼和软骨进行分割并进行三维重建。提取了软骨密度、体积和表面等几个特征。此外,还对软骨厚度分布和曲率分析进行了研究,以确定软骨状况的新指标。所有提取的特征都与先进的统计工具和机器学习一起使用,以测试我们的模型预测软骨状况的能力。这项工作是朝着基于三维测量开发软骨评估新金标准迈出的第一步。