Factors associated with meniscus volume in knees free of degenerative features.

OBJECTIVES: To explore factors that were associated with meniscus volume in knees free of radiographic osteoarthritis (OA) features and symptoms of OA. METHODS: In the third Rotterdam Study cohort, clinical, radiographic, and magnetic resonance data were obtained at baseline (BL) and after 5 years of follow-up. Meniscus volumes and their change over time were calculated after semi-automatic segmentation on Magnetic Resonance Imaging. Knees with radiographic OA features (Kellgren and Lawrence>0) or clinical diagnosis of OA (American College of Rheumatology) at BL were excluded. Ten OA risk factors were adjusted in the multivariable analysis (generalized estimating equations), treating two knees within subjects as repeated measurements. RESULTS: From 1065 knees (570 subjects), the average (standard deviation) age and Body mass index (BMI) of included subjects were 54.3 (3.7) years and 26.5 (4.4) kg/m2. At BL, nine factors (varus alignment, higher BMI, meniscus pathologies, meniscus extrusion, cartilage lesions, injury, greater physical activity level, quadriceps muscle strength, and higher age) were significantly associated with greater meniscus volume. Five factors (injury, meniscus pathologies, meniscus extrusion, higher age, and change of BMI) were significantly associated with meniscus volume loss. CONCLUSIONS: Modifiable factors (varus alignment, BMI, physical activity level, and quadriceps muscle strength) and non-modifiable factors (higher age, injury, meniscus pathologies, meniscus extrusion, and cartilage lesions) were all associated with meniscus volume or meniscus volume loss over time.

Rapid X-Ray-Based 3-D Finite Element Modeling of Medial Knee Joint Cartilage Biomechanics During Walking.

Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.

Acoustic emissions and kinematic instability of the osteoarthritic knee joint: comparison with radiographic findings.

To evaluate the acoustic emissions (AE) and kinematic instability (KI) of the osteoarthritic (OA) knee joints, and to compare these signals to radiographic findings. Sixty-six female and 43 male participants aged 44-67 were recruited. On radiography, joint-space narrowing, osteophytes and Kellgren-Lawrence (KL) grade were evaluated. Based on radiography, 54 subjects (the study group) were diagnosed with radiographic OA (KL-grade ≥ 2) while the remaining 55 subjects (KL-grade < 2) formed the control group. AE and KI were recorded with a custom-made prototype and compared with radiographic findings using area-under-curve (AUC) and independent T-test. Predictive logistic regression models were constructed using leave-one-out cross validation. In females, the parameters reflecting consistency of the AE patterns during specific tasks, KI, BMI and age had a significant statistical difference between the OA and control groups (p = 0.001-0.036). The selected AE signals, KI, age and BMI were used to construct a predictive model for radiographic OA with AUC of 90.3% (95% CI 83.5-97.2%) which showed a statistical improvement of the reference model based on age and BMI, with AUC of 84.2% (95% CI 74.8-93.6%). In males, the predictive model failed to improve the reference model. AE and KI provide complementary information to detect radiographic knee OA in females.

3D MRI in Osteoarthritis.

Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone.

OBJECTIVES: Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)-based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. METHODS: The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning-based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)-based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60-0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73-0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73-0.87). CONCLUSION: Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. KEY POINTS: • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

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.

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.

Structural risk factors for low-energy acetabular fractures.

In this study, we aimed to clarify proximal femur and acetabular structural risk factors associated with low-energy acetabular fractures in the elderly using three-dimensional (3D) computed tomography (CT). Pelvic bones and femurs were segmented and modeled in 3D from abdominopelvic CT images of 121 acetabular fracture patients (mean age 72 ±â€¯12 years, range 50-98 years, 31 females and 90 males) and 121 age-gender matched controls with no fracture. A set of geometric parameters of the proximal femur and the acetabulum was measured. An independent-samples t-test or a Mann-Whitney U test was used for statistical analyses. The fractured side was used for proximal femur geometry, while the contralateral side was used for acetabular geometry. The neck shaft angle (NSA) was significantly smaller (mean 122.1° [95% CI 121.1°-123.2°] vs. 124.6° [123.6°-125.6°], p = 0.001) and the femoral neck axis length (FNALb) was significantly longer (78.1 mm [77.0-79.2 mm] vs. 76.0 mm [74.8-77.2 mm], p = 0.026) in the fracture group than in the controls when genders were combined. The NSA was significantly smaller both for females (120.2° [117.8°-122.6°] vs. 124.7° [122.5°-127.0°], p = 0.007) and for males (122.7° [121.5°-123.8°] vs. 124.6° [123.4°-125.7°], p = 0.006) in the fracture group. However, only males showed a significantly longer FNALb (80.0 mm [78.9-81.1 mm] vs. 77.8 mm [76.6-79.0 mm], p = 0.025). No statistically significant associations of acetabular geometry with fractures were found. However, the mean values of the acetabular angle of Sharp (34°), the lateral center-edge angle (40°), the anterior center-edge angle (62°), and the posterior center-edge angle (105°) indicated possible over-coverage. In conclusion, our findings suggest that proximal femur geometry is associated with low-energy acetabular fractures. Especially elderly subjects with an NSA smaller than normal have an increased risk of acetabular fractures.

Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis.

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 < 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 < 0.001) for FDVer texture parameter and between - 0.10 and 0.97 for FDHor. Bone density and texture were different (p < 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.

Comparison of bone texture between normal individuals and patients with Kashin-Beck disease from plain radiographs in knee.

To compare tibial bone texture between Kashin-Beck disease (KBD) patients and normal individuals from plain radiographs using an advanced image analysis. Plain knee radiographs were obtained from KBD patients (n = 49) and age-matched healthy controls (n = 98). KBD were graded with diagnostic criteria WS/T 207-2010. The textural values related to bone structure from medial and lateral tibial subchondral and trabecular bones were evaluated using entropy of Laplacian-based image (ELap), entropy of local binary patterns (ELBP), homogeneity indices (HI) of local angles (HIMean, HIPerp and HIParal), and fractal dimensions from horizontal (FDHor) and vertical (FDVer) structures. KBD patients were shorter in height and lighter in weight, and their tibial width was wider than controls. Anatomical angle of KBD patients showed more genu valgus. Total KBD patients and subgroups had higher ELap, HIMean, HIPerp and HIParal in detected tibial subchondral and trabecular bones than controls, except ELap in lateral subchondral bone. ELBP, FDHor and FDVer from the detected tibial bone in KBD patients and subgroups were lower than controls, except FDVer in lateral trabecular bone. Our results indicate that micro-scale in bone texture in KBD-affected knees can be quantitatively examined from plain radiographs using an advanced image analysis.

Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study.

OBJECTIVES: To investigate whether subchondral bone structure from plain radiographs is different between subjects with and without articular cartilage damage or bone marrow lesions (BMLs). METHODS: Radiography-based bone structure was assessed from 80 subjects with different stages of knee osteoarthritis using entropy of Laplacian-based image (ELap) and local binary patterns (ELBP), homogeneity index of local angles (HIAngles,mean), and horizontal (FDHor) and vertical fractal dimensions (FDVer). Medial tibial articular cartilage damage and BMLs were scored using the magnetic resonance imaging osteoarthritis knee score. Level of statistical significance was set to p < 0.05. RESULTS: Subjects with medial tibial cartilage damage had significantly higher FDVer and ELBP as well as lower ELap and HIAngles,mean in the medial tibial subchondral bone region than subjects without damage. FDHor, FDVer, and ELBP were significantly higher, whereas ELap and HIAngles,mean were lower in the medial trabecular bone region. Subjects with medial tibial BMLs had significantly higher FDVer and ELBP as well as lower ELap and HIAngles,mean in medial tibial subchondral bone. FDHor, FDVer, and ELBP were higher, whereas ELap and HIAngles,mean were lower in medial trabecular bone. CONCLUSIONS: Our results support the use of bone structural analysis from radiographs when examining subjects with osteoarthritis or at risk of having it. KEY POINTS: • Knee osteoarthritis causes changes in articular cartilage and subchondral bone • Magnetic resonance imaging is a comprehensive imaging modality for knee osteoarthritis • Radiography-based bone structure analysis can provide additional information of osteoarthritic subjects.

Correlation of Subchondral Bone Density and Structure from Plain Radiographs with Micro Computed Tomography Ex Vivo.

Osteoarthritis causes changes in the subchondral bone structure and composition. Plain radiography is a cheap, fast, and widely available imaging method. Bone tissue can be well seen from plain radiograph, which however is only a 2D projection of the actual 3D structure. Therefore, the aim was to investigate the relationship between bone density- and structure-related parameters from 2D plain radiograph and 3D bone parameters assessed from micro computed tomography (µCT) ex vivo. Right tibiae from eleven cadavers without any diagnosed joint disease were imaged using radiography and with µCT. Bone density- and structure-related parameters were calculated from four different locations from the radiographs of proximal tibia and compared with the volumetric bone microarchitecture from the corresponding regions. Bone density from the plain radiograph was significantly related with the bone volume fraction (r = 0.86; n = 44; p < 0.01). Mean homogeneity index for orientation of local binary patterns (HI(angle,mean)) and fractal dimension of vertical structures (FD(Ver)) were related (p < 0.01) with connectivity density (HI(angle,mean): r = -0.73, FD(Ver): r = 0.69) and trabecular separation (HI(angle,mean): r = 0.73, FD(Ver): r = -0.70) when all ROIs were pooled together (n = 44). Bone density and structure in tibia from standard clinically available 2D radiographs are significantly correlated with true 3D microstructure of bone.

Association between quantitative MRI and ICRS arthroscopic grading of articular cartilage.

PURPOSE: To investigate the association of quantitative magnetic resonance imaging (qMRI) parameters with arthroscopic grading of cartilage degeneration. Arthroscopy of the knee is considered to be the gold standard of osteoarthritis diagnostics; however, it is operator-dependent and limited to the evaluation of the articular surface. qMRI provides information on the quality of articular cartilage and its changes even at early stages of a disease. METHODS: qMRI techniques included T 1 relaxation time, T 2 relaxation time, and delayed gadolinium-enhanced MRI of cartilage mapping at 3 T in ten patients. Due to a lack of generally accepted semiquantitative scoring systems for evaluating severity of cartilage degeneration during arthroscopy, the International Cartilage Repair Society (ICRS) classification system was used to grade the severity of cartilage lesions. qMRI parameters were statistically compared to arthroscopic grading conducted with the ICRS classification system. RESULTS: qMRI parameters were not linearly related to arthroscopic grading. Spearman's correlation coefficients between qMRI and arthroscopic grading were not significant. The relative differences in qMRI parameters of superficial and deep cartilage varied with degeneration, suggesting different macromolecular alterations in different cartilage zones. CONCLUSIONS: Results suggest that loss of cartilage and the quality of remaining tissue in the lesion site may not be directly associated with each other. The severity of cartilage degeneration may not be revealed solely by diagnostic arthroscopy, and thus, qMRI can have a role in the investigation of cartilage degeneration.

Ultrasound arthroscopy of human knee cartilage and subchondral bone in vivo.

Arthroscopic ultrasound imaging enables quantitative evaluation of articular cartilage. However, the potential of this technique for evaluation of subchondral bone has not been investigated in vivo. In this study, we address this issue in clinical arthroscopy of the human knee (n = 11) by determining quantitative ultrasound (9 MHz) reflection and backscattering parameters for cartilage and subchondral bone. Furthermore, in each knee, seven anatomical sites were graded using the International Cartilage Repair Society (ICRS) system based on (i) conventional arthroscopy and (ii) ultrasound images acquired in arthroscopy with a miniature transducer. Ultrasound enabled visualization of articular cartilage and subchondral bone. ICRS grades based on ultrasound images were higher (p < 0.05) than those based on conventional arthroscopy. The higher ultrasound-based ICRS grades were expected as ultrasound reveals additional information on, for example, the relative depth of the lesion. In line with previous literature, ultrasound reflection and scattering in cartilage varied significantly (p < 0.05) along the ICRS scale. However, no significant correlation between ultrasound parameters and structure or density of subchondral bone could be demonstrated. To conclude, arthroscopic ultrasound imaging had a significant effect on clinical grading of cartilage, and it was found to provide quantitative information on cartilage. The lack of correlation between the ultrasound parameters and bone properties may be related to lesser bone change or excessive attenuation in overlying cartilage and insufficient power of the applied miniature transducer.

Assessment of risk of femoral neck fracture with radiographic texture parameters: a retrospective study.

PURPOSE: To investigate whether femoral neck fracture can be predicted retrospectively on the basis of clinical radiographs by using the combined analysis of bone geometry, textural analysis of trabecular bone, and bone mineral density (BMD). MATERIALS AND METHODS: Formal ethics committee approval was obtained for the study, and all participants gave informed written consent. Pelvic radiographs and proximal femur BMD measurements were obtained in 53 women aged 79-82 years in 2006. By 2012, 10 of these patients had experienced a low-impact femoral neck fracture. A Laplacian-based semiautomatic custom algorithm was applied to the radiographs to calculate the texture parameters along the trabecular fibers in the lower neck area for all subjects. Intra- and interobserver reproducibility was calculated by using the root mean square average coefficient of variation to evaluate the robustness of the method. RESULTS: The best predictors of hip fracture were entropy (P = .007; reproducibility coefficient of variation < 1%), the neck-shaft angle (NSA) (P = .017), and the BMD (P = .13). For prediction of fracture, the area under the receiver operating characteristic curve was 0.753 for entropy, 0.608 for femoral neck BMD, and 0.698 for NSA. The area increased to 0.816 when entropy and NSA were combined and to 0.902 when entropy, NSA, and BMD were combined. CONCLUSION: Textural analysis of pelvic radiographs enables discrimination of patients at risk for femoral neck fracture, and our results show the potential of this conventional imaging method to yield better prediction than that achieved with dual-energy x-ray absorptiometry-based BMD. The combination of the entropy parameter with NSA and BMD can further enhance predictive accuracy.

Trabecular homogeneity index derived from plain radiograph to evaluate bone quality.

Radiographic texture analysis has been developed lately to improve the assessment of bone architecture as a determinant of bone quality. We validate here an algorithm for the evaluation of trabecular homogeneity index (HI) in the proximal femur from hip radiographs, with a focus on the impact of the principal compressive system of the trabecular bone, and evaluate its correlation with femoral strength, bone mineral density (BMD), and volumetric trabecular structure parameters. A semiautomatic custom-made algorithm was applied to calculate the HI in the femoral neck and trochanteric areas from radiographs of 178 femoral bone specimens (mean age 79.3 ± 10.4 years). Corresponding neck region was selected in CT scans to calculate volumetric parameters of trabecular structure. The site-specific BMDs were assessed from dual-energy X-ray absorptiometry (DXA), and the femoral strength was experimentally tested in side-impact configuration. Regression analysis was performed between the HI and biomechanical femoral strength, BMD, and volumetric parameters. The correlation between HI and failure load was R(2) = 0.50; this result was improved to R(2) = 0.58 for cervical fractures alone. The discrimination of bones with high risk of fractures (load <3000 N) was similar for HI and BMD (AUC = 0.87). Regression analysis between the HIs versus site-specific BMDs yielded R(2) = 0.66 in neck area, R(2) = 0.60 in trochanteric area, and an overall of R(2) = 0.66 for the total hip. Neck HI and BMD correlated significantly with volumetric structure parameters. We present here a method to assess HI that can explain 50% of an experimental failure load and determines bones with high fracture risk with similar accuracy as BMD. The HI also had good correlation with DXA and computed tomography-derived data.