The growth of the mastoid volume in children with a cochlear implant.

The aim of this study was to understand the mastoid volume development in children who undergo cochlear implantation surgery. Cochlear implant (CI) database of our clinic (Kuopio University Hospital) was reviewed for computed tomography (CT) images of CI patients (age under 12 years at the time of implantation) with a minimum time interval of twelve months between their pre- and postoperative CT. Eight patients (nine ears) were found eligible for inclusion. Three linear measurements were taken by using picture archiving and communication systems (PACS) software and the volume of the MACS was measured with Seg 3D software. The mastoid volume increased on average 817.5 mm3 between the pre- and the postoperative imaging time point. The linear distances measured between anatomical points like the round window (RW)- bony ear canal (BEC), the RW-sigmoid sinus (SS), the BEC-SS, and the mastoid tip (MT)-superior semicircular canal (SSC) increased significantly with the age of the patient at both the pre-op and post-op time points. The linear measurements between key anatomical points and mastoid volume showed a positive linear correlation. The correlation between linear measurement and volume were significant between the MT-SSC (r = 0.706, p = 0.002), RW-SS (r = 0.646, p = 0.005) and RW-BEC (r = 0.646, p = 0.005). Based on our findings from the CI implanted patients and comparing it with the previous literature findings from non-CI implanted patients, we could say that the CI surgery seem to have no effect on the development of mastoid volume in children.

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.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint.

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Computational evaluation of altered biomechanics related to articular cartilage lesions observed in vivo.

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 ρ = 0.733-0.917). Depth and volume of the lesion correlated also with the maximum principal strain (p < 0.05, ρ = 0.767, and ρ = 0.717, respectively). However, the lesion area had non-significant correlation with this strain parameter (p = 0.06, ρ = 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 osteoarthritis. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images.

Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79-0.83 and sensitivity and specificity values were also high (0.76-0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient = 0.85-0.93); the difference in thickness (mean ± SD) was 0.27 ± 0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450 ± 190 to 42 ± 10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.

Quantitative Dual Contrast CT Technique for Evaluation of Articular Cartilage Properties.

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 (R2= 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 Vivo Contrast-Enhanced Cone Beam CT Provides Quantitative Information on Articular Cartilage and Subchondral Bone.

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.