Comparison of ultrasonographic, radiographic and intra-operative findings in severe hip osteoarthritis

Aim of this study was to assess the US findings of patients with late-stage hip OA undergoing total hip arthroplasty (THA), and to associate the US findings with conventional radiography (CR) and intraoperative findings. Moreover, the inter-rater reliability of hip US, and association between the US and Oxford Hip Score (OHS) were evaluated. Sixty-eight hips were included, and intraoperative findings were available on 48 hips. Mean patient age was 67.6 years and 38% were males. OA findings—osteophytes at femoral collum and anterosuperior acetabulum, femoral head deformity and effusion—were assessed on US, CR and THA. The diagnostic performance of US and CR was compared by applying the THA findings as the gold standard. Osteoarthritic US findings were very common, but no association between the US findings and OHS was observed. The pooled inter-rater reliability (n = 65) varied from moderate to excellent (k = 0.538–0.815). When THA findings were used as the gold standard, US detected femoral collum osteophytes with 95% sensitivity, 0% specificity, 81% accuracy, and 85% positive predictive value. Concerning acetabular osteophytes, the respective values were 96%, 0%, 88% and 91%. For the femoral head deformity, they were 92%, 36%, 38% and 83%, and for the effusion 49%, 85%, 58% and 90%, respectively. US provides similar detection of osteophytes as does CR. On femoral head deformity, performance of the US is superior to CR. The inter-rater reliability of the US evaluation varies from moderate to excellent, and no association between US and OHS was observed in this patient cohort.</p

Ultrasonographic Assessment of the Normal Femoral Articular Cartilage of the Knee Joint: Comparison with 3D MRI

ObjectiveUltrasonography (US) has a promising role in evaluating the knee joint, but capability to visualize the femoral articular cartilage needs systematic evaluation. We measured the extent of this acoustic window by comparing standardized US images with the corresponding MRI views of the femoral cartilage.DesignTen healthy volunteers without knee pathology underwent systematic US and MRI evaluation of both knees. The femoral cartilage was assessed on the oblique transverse axial plane with US and with 3D MRI. The acoustic window on US was compared to the corresponding views of the femoral sulcus and both condyles on MRI. The mean imaging coverage of the femoral cartilage and the cartilage thickness measurements on US and MRI were compared.ResultsMean imaging coverage of the cartilage of the medial femoral condyle was 66% (range 54%–80%) and on the lateral femoral condyle 37% (range 25%–51%) compared with MRI. Mean cartilage thickness measurement in the femoral sulcus was 3.17 mm with US and 3.61 mm with MRI (14.0% difference). The corresponding measurements in the medial femoral condyle were 1.95 mm with US and 2.35 mm with MRI (21.0% difference), and in the lateral femoral condyle, they were 2.17 mm and 2.73 mm (25.6% difference), respectively.ConclusionTwo-thirds of the articular cartilage of the medial femoral condyle, and one-third in the lateral femoral condyle, can be assessed with US. The cartilage thickness measurements seem to be underestimated by US. These results show promise for the evaluation of the weight-bearing cartilage of the medial femoral condyle with US.</p

Effects of Articular Cartilage Constituents on Phosphotungstic Acid Enhanced Micro-Computed Tomography

Contrast-enhanced micro-computed tomography (CE mu CT) with phosphotungstic acid (PTA) has shown potential for detecting collagen distribution of articular cartilage. However, the selectivity of the PTA staining to articular cartilage constituents remains to be elucidated. The aim of this study was to investigate the dependence of PTA for the collagen content in bovine articular cartilage. Adjacent bovine articular cartilage samples were treated with chondroitinase ABC and collagenase to degrade the proteoglycan and the collagen constituents in articular cartilage, respectively. Enzymatically degraded samples were compared to the untreated samples using CE mu CT and reference methods, such as Fourier-transform infrared imaging. Decrease in the X-ray attenuation of PTA in articular cartilage and collagen content was observed in cartilage depth of 0-13% and deeper in tissue after collagen degradation. Increase in the X-ray attenuation of PTA was observed in the cartilage depth of 13- 39% after proteoglycan degradation. The X-ray attenuation of PTA-labelled articular cartilage in CE mu CT is associated mainly with collagen content but the proteoglycans have a minor effect on the X-ray attenuation of the PTA-labelled articular cartilage. In conclusion, the PTA labeling provides a feasible CE mu CT method for 3D characterization of articular cartilage.Peer reviewe

An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data

X-ray tomography is a reliable tool for determining the inner structure of 3-D object with penetrating X-rays. However, traditional reconstruction methods, such as Feldkamp-Davis-Kress (FDK), require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high-resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3-D reconstruction from such a sparsely sampled data set is difficult because the measurement data are usually contaminated by errors, and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the 3-D shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted as 0 <C-prPeer reviewe

Contrast-Enhanced Computed Tomography Enables Quantitative Evaluation of Tissue Properties at Intrajoint Regions in Cadaveric Knee Cartilage

Objective: The aim of this study was to investigate whether the concentration of the anionic contrast agent ioxaglate, as quantitated by contrast-enhanced computed tomography (CECT) using a clinical cone-beam CT (CBCT) instrument, reflects biochemical, histological, and biomechanical characteristics of articular cartilage imaged in an ex vivo, intact human knee joint. Design: An osteoarthritic human cadaveric knee joint (91 years old) was injected with ioxaglate (36 mg I/mL) and imaged using CBCT over 61 hours of ioxaglate diffusion into cartilage. Following imaging, the joint surfaces were excised, rinsed to remove contrast agent, and compressive stiffness (equilibrium and instantaneous compressive moduli) was measured via indentation testing (n = 17 sites). Each site was sectioned for histology and assessed for glycosaminoglycan content using digital densitometry of Safranin-O stained sections, Fourier transform infrared spectroscopy for collagen content, and morphology using both the Mankin and OARSI semiquantitative scoring systems. Water content was determined using mass change after lyophilization. Results: CECT attenuation at all imaging time points, including those <1 hour of ioxaglate exposure, correlated significantly (P < 0.05) with cartilage water and glycosaminoglycan contents, Mankin score, and both equilibrium and instantaneous compressive moduli. Early time points (<30 minutes) also correlated (P < 0.05) with collagen content and OARSI score. Differences in cartilage quality between intrajoint regions were distinguishable at diffusion equilibrium and after brief ioxaglate exposure. Conclusions: CECT with ioxaglate affords biochemical and biomechanical measurements of cartilage health and performance even after short, clinically relevant exposure times, and may be useful in the clinic as a means for detecting early signs of cartilage pathology

Fourier Transform Infrared Spectroscopic Imaging and Multivariate Regression for Prediction of Proteoglycan Content of Articular Cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O –stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used

Magnetic-Responsive Carbon Nanotubes Composite Scaffolds for Chondrogenic Tissue Engineering

The demand for engineered scaffolds capable of delivering multiple cues to cells continues to grow as the interplay between cell fate with microenvironmental and external cues is revealed. Emphasis has been given to develop stimuli-responsive scaffolds. These scaffolds are designed to sense an external stimulus triggering a specific response (e.g., change in the microenvironment, release therapeutics, etc.) and then initiate/modulate a desired biofunction. Here, magnetic-responsive carboxylated multi-walled carbon nanotubes (cMWCNTs) are integrated into 3D collagen/polylactic acid (PLA) scaffold via a reproducible filtration-based method. The integrity and biomechanical performance of the collagen/PLA scaffolds are preserved after cMWCNT integration. In vitro safety assessment of cMWCNT/collagen/PLA scaffolds shows neither cytotoxicity effects nor macrophage pro-inflammatory response, supporting further in vitro studies. The cMWCNT/collagen/PLA scaffolds enhance chondrocytes metabolic activity while maintaining high cell viability and extracellular matrix (i.e., type II collagen and aggrecan) production. Comprehensive in vitro study applying static and pulsed magnetic field on seeded scaffolds shows no specific cell response in dependence with the applied field. This result is independent of the presence or absence of cMWCNT into the collagen/PLA scaffolds. Taken together, these findings provide additional evidence of the benefits to exploit the CNTs outstanding properties in the design of stimuli-responsive scaffolds.publishedVersio

3D morphometric analysis of calcified cartilage properties using micro-computed tomography

OBJECTIVE: Our aim is to establish methods for quantifying morphometric properties of calcified cartilage (CC) from micro-computed tomography (muCT). Furthermore, we evaluated the feasibility of these methods in investigating relationships between osteoarthritis (OA), tidemark surface morphology and open subchondral channels (OSCCs). METHOD: Samples (n = 15) used in this study were harvested from human lateral tibial plateau (n = 8). Conventional roughness and parameters assessing local 3-dimensional (3D) surface variations were used to quantify the surface morphology of the CC. Subchondral channel properties (percentage, density, size) were also calculated. As a reference, histological sections were evaluated using Histopathological osteoarthritis grading (OARSI) and thickness of CC and subchondral bone (SCB) was quantified. RESULTS: OARSI grade correlated with a decrease in local 3D variations of the tidemark surface (amount of different surface patterns (rs = -0.600, P = 0.018), entropy of patterns (EP) (rs = -0.648, P = 0.018), homogeneity index (HI) (rs = 0.555, P = 0.032)) and tidemark roughness (TMR) (rs = -0.579, P = 0.024). Amount of different patterns (ADP) and EP associated with channel area fraction (CAF) (rp = 0.876, P < 0.0001; rp = 0.665, P = 0.007, respectively) and channel density (CD) (rp = 0.680, P = 0.011; rp = 0.582, P = 0.023, respectively). TMR was associated with CAF (rp = 0.926, P < 0.0001) and average channel size (rp = 0.574, P = 0.025). CC topography differed statistically significantly in early OA vs healthy samples. CONCLUSION: We introduced a mu-CT image method to quantify 3D CC topography and perforations through CC. CC topography was associated with OARSI grade and OSCC properties; this suggests that the established methods can detect topographical changes in tidemark and CC perforations associated with OA

Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks

Abstract Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows performing independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren–Lawrence (KL) composite score. In this study, we developed an automatic method to predict KL and OARSI grades from knee radiographs. Our method is based on Deep Learning and leverages an ensemble of residual networks with 50 layers. We used transfer learning from ImageNet with a fine-tuning on the Osteoarthritis Initiative (OAI) dataset. An independent testing of our model was performed on the Multicenter Osteoarthritis Study (MOST) dataset. Our method yielded Cohen’s kappa coefficients of 0.82 for KL-grade and 0.79, 0.84, 0.94, 0.83, 0.84 and 0.90 for femoral osteophytes, tibial osteophytes and joint space narrowing for lateral and medial compartments, respectively. Furthermore, our method yielded area under the ROC curve of 0.98 and average precision of 0.98 for detecting the presence of radiographic OA, which is better than the current state-of-the-art

Kneel:knee anatomical landmark localization using hourglass networks

Abstract This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA). Landmark localization can be viewed as regression problem, where the landmark position is directly predicted by using the region of interest or even full-size images leading to large memory footprint, especially in case of high resolution medical images. In this work, we propose an efficient deep neural networks framework with an hourglass architecture utilizing a soft-argmax layer to directly predict normalized coordinates of the landmark points. We provide an extensive evaluation of different regularization techniques and various loss functions to understand their influence on the localization performance. Furthermore, we introduce the concept of transfer learning from low-budget annotations, and experimentally demonstrate that such approach is improving the accuracy of landmark localization. Compared to the prior methods, we validate our model on two datasets that are independent from the train data and assess the performance of the method for different stages of OA severity. The proposed approach demonstrates better generalization performance compared to the current state-of-the-art