Rapid CT-based Estimation of Articular Cartilage Biomechanics in the Knee Joint Without Cartilage Segmentation

Knee osteoarthritis (OA) is a painful joint disease, causing disabilities in daily activities. However, there is no known cure for OA, and the best treatment strategy might be prevention. Finite element (FE) modeling has demonstrated potential for evaluating personalized risks for the progression of OA. Current FE modeling approaches use primarily magnetic resonance imaging (MRI) to construct personalized knee joint models. However, MRI is expensive and has lower resolution than computed tomography (CT). In this study, we extend a previously presented atlas-based FE modeling framework for automatic model generation and simulation of knee joint tissue responses using contrast agent-free CT. In this method, based on certain anatomical dimensions measured from bone surfaces, an optimal template is selected and scaled to generate a personalized FE model. We compared the simulated tissue responses of the CT-based models with those of the MRI-based models. We show that the CT-based models are capable of producing similar tensile stresses, fibril strains, and fluid pressures of knee joint cartilage compared to those of the MRI-based models. This study provides a new methodology for the analysis of knee joint and cartilage mechanics based on measurement of bone dimensions from native CT scans

Rapid CT-based Estimation of Articular Cartilage Biomechanics in the Knee Joint Without Cartilage Segmentation

Knee osteoarthritis (OA) is a painful joint disease, causing disabilities in daily activities. However, there is no known cure for OA, and the best treatment strategy might be prevention. Finite element (FE) modeling has demonstrated potential for evaluating personalized risks for the progression of OA. Current FE modeling approaches use primarily magnetic resonance imaging (MRI) to construct personalized knee joint models. However, MRI is expensive and has lower resolution than computed tomography (CT). In this study, we extend a previously presented atlas-based FE modeling framework for automatic model generation and simulation of knee joint tissue responses using contrast agent-free CT. In this method, based on certain anatomical dimensions measured from bone surfaces, an optimal template is selected and scaled to generate a personalized FE model. We compared the simulated tissue responses of the CT-based models with those of the MRI-based models. We show that the CT-based models are capable of producing similar tensile stresses, fibril strains, and fluid pressures of knee joint cartilage compared to those of the MRI-based models. This study provides a new methodology for the analysis of knee joint and cartilage mechanics

The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.</p

Novel X-ray-based methods for diagnostics of osteoarthritis

Abstract Osteoarthritis (OA) is the commonest joint disease in the world, and it has a major socioeconomic impact. OA causes progressive degenerative changes in the composition and structure of articular cartilage and subchondral bone. Clinical diagnosis of OA is based on physical examination and qualitative evaluation of changes on plain radiographs. Current clinical imaging methods are subjective or insensitive to early OA changes. Therefore, new methods are needed so as to quantify composition of the cartilage and characteristics of the subchondral bone. The aim of this thesis was to evaluate the potential of clinically applicable X-ray-based methods for the assessment of the cartilage proteoglycan content as well as the structure and density of subchondral bone in a knee joint. Subchondral bone density and structure (local binary patterns, Laplacian, and fractal-based algorithms) analysis methods for two-dimensional (2-D) plain radiographs were validated against three-dimensional (3-D) bone microarchitecture obtained from micro-computed tomography ex vivo and applied to plain radiographs in vivo. Furthermore, a method developed for the evaluation of articular cartilage proteoglycan content from computed tomography (CT) was validated against a delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), which is widely used as a proteoglycan sensitive method, in subjects referred for an arthroscopy of the knee joint. Subchondral bone density and structure evaluated from 2-D radiographs were significantly related to the bone volume fraction and true 3-D microarchitecture of bone, respectively. In addition, bone density- and structure-related parameters from radiographs were significantly different among subjects with different stages of OA. Cartilage proteoglycan content evaluated from CT was significantly related to dGEMRIC method. Furthermore, dGEMRIC was associated with bone structure from a 2-D radiograph. In conclusion, analysis of bone structure and density is feasible from clinically available 2-D radiographs. A novel CT method sensitive to proteoglycan content should be considered when a 3-D view of cartilage quality is needed.Tiivistelmä Nivelrikko on maailman yleisin nivelsairaus. Se aiheuttaa merkittävää kärsimystä potilaille, ja sillä on suuri taloudellinen vaikutus yhteiskuntaan. Nivelrikko aiheuttaa palautumattomia muutoksia nivelrustokudoksen ja rustonalaisen luun koostumukseen ja rakenteeseen. Nivelrikon diagnoosi perustuu kliiniseen tutkimukseen ja röntgenkuvien silmämääräiseen arviointiin. Nykyiset nivelrikon kliiniset kuvantamismenetelmät ovat subjektiivisia eivätkä riittävän tarkkoja nivelrikon varhaisten muutosten osoittamiseen, minkä vuoksi rustokudoksen koostumuksen ja rustonalaisen luun muutosten arviointiin tarvitaan uusia menetelmiä. Tämän väitöskirjantyön tarkoituksena oli tutkia uusien röntgensäteilyyn perustuvien menetelmien soveltuvuutta polvinivelen rustokudoksen proteoglykaanipitoisuuden sekä luun tiheyden ja rakenteen arviointiin. Rustonalaisen luun tiheyttä ja rakennetta arvioitiin digitaalisesta röntgenkuvasta tietokonepohjaisilla menetelmillä ja tuloksia verrattiin mikrotietokonetomografiassa nähtävään luun kolmiulotteiseen rakenteeseen. Röntgenkuvasta laskettavia muuttujia verrattiin myös eriasteisesta nivelrikosta kärsivien henkilöiden välillä. Rustokudoksen proteoglykaanipitoisuutta epäsuorasti mittaavaa tietokonetomografiamenetelmää verrattiin vastaavaan magneettikuvausmenetelmään henkilöillä, jotka olivat menossa polven niveltähystykseen. Röntgenkuvasta laskettu rustonalaisen luun tiheys ja rakenne olivat tilastollisesti selkeästi yhteydessä luun tilavuusmäärään ja mikrorakenteeseen, ja ne erosivat eriasteisesta nivelrikosta kärsivien henkilöiden välillä. Proteoglykaanipitoisuutta arvioivien tietokonetomografia- ja magneettikuvausmenetelmien välillä oli tilastollisesti merkitsevä korrelaatio. Ruston proteoglykaanipitoisuutta arvioivan magneettikuvausmenetelmän ja röntgenkuvasta laskettavan luun rakenteen välillä oli myös tilastollinen yhteys. Loppupäätelmänä voidaan todeta, että luun tiheyttä ja rakennetta on mahdollista arvioida kliinisesti saatavilla olevista röntgenkuvista. Tietokonetomografiamenetelmän käyttöä tulee harkita tutkimuksissa silloin, kun rustokudoksen tilasta halutaan kolmiulotteista tietoa

Bone density and texture from minimally post-processed knee radiographs in subjects with knee osteoarthritis

Abstract Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p &lt; 0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p &lt;0.001) for FDVer texture parameter and between −0.10 and 0.97 for FDHor. Bone density and texture were different (p &lt; 0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing

Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics

The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures

Volumetric assessment of bone microstructures by a 3D local binary patterns -based method:bone changes with osteoarthritis

Abstract Osteoarthritis (OA) causes progressive degeneration of articular cartilage and pathological changes in subchondral bone, conventionally assessed volumetrically using micro-computed tomography (μCT) imaging in vitro. The local binary patterns (LBP) method has recently been suggested as a new alternative solution to perform analysis of local bone structures from μCT scans. In this study, a novel 3D LBP-based method to provide a new lead in bone microstructural analysis is proposed. In addition to the detailed description of the method, this solution is tested using µCT data of OA human trabecular bone samples, harvested from patients treated with total knee arthroplasty. The method was applied to correlate the distribution of orientations of local patterns with the severity of the disease. The local orientations of the bone fibers changed along the severity of OA, suggesting an adaptation of the bone to the disease. The structural parameters derived from the process were able to provide a new approach for the assessment of the disease, supporting the potential of this volumetric LBP-based method to assess trabecular bone changes