Impact injuries of cartilage may initiate post-traumatic degeneration, making early detection of injury imperative for timely surgical or pharmaceutical interventions. Cationic (positively-charged) CT contrast agents detect loss of cartilage proteoglycans (PGs) more sensitively than anionic (negatively-charged) or non-ionic (non-charged, i.e., electrically neutral) agents. However, degeneration related loss of PGs and increase in water content have opposite effects on the diffusion of the cationic agent, lowering its sensitivity. In contrast to cationic agents, diffusion of non-ionic agents is governed only by steric hindrance and water content of cartilage. We hypothesize that sensitivity of an iodine(I)-based cationic agent may be enhanced by simultaneous use of a non-ionic gadolinium(Gd)-based agent. We introduce a quantitative dual energy CT technique (QDECT) for simultaneous quantification of two contrast agents in cartilage. We employ this technique to improve the sensitivity of cationic CA4+ (q =+4) by normalizing its partition in cartilage with that of non-ionic gadoteridol. The technique was evaluated with measurements of contrast agent mixtures of known composition and human osteochondral samples (n = 57) after immersion (72 h) in mixture of CA4+ and gadoteridol. Samples were arthroscopically graded and biomechanically tested prior to QDECT (50/100 kV). QDECT determined contrast agent mixture compositions correlated with the true compositions (R= 0.99, average error = 2.27%). Normalizing CA4+ partition in cartilage with that of gadoteridol improved correlation with equilibrium modulus (from ρ = 0.701 to 0.795). To conclude, QDECT enables simultaneous quantification of I and Gd contrast agents improving diagnosis of cartilage integrity and biomechanical status.

In post-traumatic osteoarthritis, both articular cartilage and subchondral bone undergo characteristic pathological changes. This study investigates potential of delayed cone beam computed tomography arthrography (dCBCTa) to simultaneously detect variations in cartilage and subchondral bone. The knees of patients (n = 17) with suspected joint injuries were imaged using a clinical CBCT scanner at 5 and 45 min after the intra-articular injection of anionic contrast agent (Hexabrix™) with hydroxyapatite phantoms around the knee. Normalized attenuation (i.e., contrast agent partition, an indicator of tissue composition) in cartilage, bone mineral density (BMD) in subchondral bone plate (SBP), subchondral bone and trabecular bone, and thicknesses of SBP and cartilage were determined. Lesions of cartilage were scored using International Cartilage Repair Society (ICRS) grading. Normalized attenuation in the delayed image (t = 45 min) increased along the increase of ICRS grade (p = 0.046). Moreover, BMD was significantly higher in SBPs under damaged cartilage (ICRS = 1-2 or ICRS ≥ 3; p = 0.047 and p = 0.038, respectively) than in SBP under non-injured tissue (ICRS = 0). For the first time, dCBCTa enabled the detection of articular cartilage injuries and subchondral bone alterations simultaneously in vivo. Significant relations between ICRS grading and both cartilage and bone parameters suggest that dCBCTa has potential for quantitative imaging of the knee joint.

Chondral lesions provide a potential risk factor for development of osteoarthritis. Despite the variety of in vitro studies on lesion degeneration, in vivo studies that evaluate relation between lesion characteristics and the risk for the possible progression of OA are lacking. Here, we aimed to characterize different lesions and quantify biomechanical responses experienced by surrounding cartilage tissue. We generated computational knee joint models with nine chondral injuries based on clinical in vivo arthrographic computed tomography images. Finite element models with fibril-reinforced poro(visco)elastic cartilage and menisci were constructed to simulate physiological loading. Systematically, the lesions experienced increased peak values of maximum principal strain, maximum shear strain, and minimum principal strain in the surrounding chondral tissue (p < 0.01) compared with intact tissue. Depth, volume, and area of the lesion correlated with the maximum shear strain (p < 0.05, Spearman rank correlation coefficient r ¼ 0.733-0.917). Depth and volume of the lesion correlated also with the maximum principal strain (p < 0.05, r ¼ 0.767, and r ¼ 0.717, respectively). However, the lesion area had non-significant correlation with this strain parameter (p ¼ 0.06, r ¼ 0.65). Potentially, the introduced approach could be developed for clinical evaluation of biomechanical risks of a chondral lesion and planning an intervention. Statement of Clinical Relevance:In this study, we computationally characterized different in vivo chondral lesions and evaluated their risk of cartilage degeneration. This information is vital in decision-making for intervention in order to prevent post-traumatic

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.