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

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.

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration

“…30 They analyzed the data (among which, as anticipated, there are no 3D features similar to those object of this paper) of 3872 subjects extracted from a public database demonstrating a larger amount of the most informative features belongs to three categories (namely, subject characteristics, symptoms and physical exam). When fed to several ML algorithm (the feature selection results were tuned for each algorithm), the authors found promising scores (however, considering SVM, one of the best ones, the scores demonstrated less promising than those showed in this paper or comparable to the best one achieved in the previous paper, 4 suggesting this methodology could represent a promising example of increasing the understanding of the rationale behind the decision-making mechanism of the selected ML model and the impact of the used risk factors on the prediction output. Although this paper presented several promising findings, these results are not conclusive, since few limitations must be considered.…”

“…A database containing knee radiographical images and anatomical 3D reconstruction of all the patients was developed and is available at https://restore-project.ru.is. The subjects enrolled for this study and the recruitment process were the same described in the previous paper, 4 with slight modifications. Specifically, while the considered Degenerative (D) group (24 subjects, mean age = 64 years, std age = 12 years) was considered as-is, as anticipated the traumatic + control groups -herein labeled as "Non Degenerative" (ND) -were considered as a unique group (23 patients, mean age = 35 years, std age = 12 years).…”

“…The acquired areas of interest were the cartilage-covered areas around the knee (14 cm centered at the knee joint). The results of the acquisitions were volumetric 3D sequences (with isotropic voxels of 0.6 mm) acquired in the axial plane Data Processing As already described in our previous paper, 4 the medical 3D modeling software Materialise MIMICS (Materialise Interactive Medical Image Control System, Materialise, Belgium) was used to segment the acquired images -the same protocols for bones and cartilages, respectively taken from CT scan and MRI, was followed -of each acquisition and to extract 24 parameters per patient. The considered bones are femur, tibia and patella, while the cartilages are femoral, lateral and medial tibial and patellar.…”

“…In this context, starting from the approach presented in the previous paper, 4 the purpose of this work is to remark the predictive power of several of the features already presented, but investigating their straightness considering simpler Machine Learning (ML) models and setting up a binary classification study (considering both the controls and traumatic patients in the same groups since the non-crucial differences in the statistical analyses in the previous paper). 4 …”

“…Assessment of cartilage condition is therefore crucial for both detecting and monitoring the progression of OA, and in recent years several novel strategies (also based on artificial intelligence) 3 have been proposed to this aim. In this context, starting from the approach presented in the previous paper, 4 the purpose of this work is to remark the predictive power of several of the features already presented, but investigating their straightness considering simpler Machine Learning (ML) models and setting up a binary classification study (considering both the controls and traumatic patients in the same groups since the non-crucial differences in the statistical analyses in the previous paper). 4 The findings suggest the overall workflow (presented in this and the previous paper) 4 may represent in the future a potential advanced platform for cartilage diagnosis.…”