Articular Cartilage Evaluation After TruFit Plug Implantation Analyzed by Delayed Gadolinium-Enhanced MRI of Cartilage (dGEMRIC)

Background: Quantitative MRI of articular cartilage has rapidly developed in recent years and provides the clinician with a noninvasive tool to determine the biological consequence of an intervention. Purpose: To evaluate the quality of intra-articular cartilage, using the dGEMRIC scanning technique, 1 year after TruFit implantation. The hypothesis was that implantation of a TruFit plug does not lead to damage at the opposing articular cartilage. Study Design: Case series; Level of evidence, 4. Methods: A total of 13 patients (age, 32 ± 8 years) were evaluated with dGEMRIC at 12 ± 4 months after treatment of an osteochondral lesion by implantation of 1 or multiple TruFit plugs. The dGEMRIC scanning protocol was applied 90 minutes after intravenous Magnevist (0.2 mmol/kg body weight) injection. Different regions of interest (ROIs) were defined: the femur cartilage, cartilage directly surrounding the implanted TruFit plug, the TruFit plug, and the articulating and nonarticulating tibia cartilage. The average dGEMRIC index (T1gd; magnetic resonance imaging relaxation time per ROI) was calculated by a pixel-by-pixel curve fitting using the Levenberg-Marquardt method. Differences between the mean T1gd of the individual ROI for all patients were tested using analysis of variance with post hoc Bonferroni correction. A P value <.05 was considered statistically significant. Results: The average T1gd of the TruFit ROI (385 ± 74 ms) was comparable with those in the femur (409 ± 49 ms) and surrounding (392 ± 64 ms) ROIs (P ≥ .339). The average T1gds for the articulating (578 ± 133 ms) and nonarticulating (516 ± 118 ms) ROIs were higher compared with the femur (409 ± 49 ms), surrounding (392 ± 64 ms), and TruFit (385 ± 74 ms) ROIs (P < .002), while no difference was observed between the tibia ROIs (P = .160). Conclusion: Implantation of the TruFit plug in osteochondral lesions does not damage the opposing or surrounding surface, and newly formed tissue inside the plug has cartilage-like dGEMRIC characteristics 12 months after implantation. The implantation of synthetic TruFit plugs is safe for the opposing cartilage, an item that is frequently discussed when using such materials to treat focal cartilage defects

Knee Images Digital Analysis (KIDA): a novel method to quantify individual radiographic features of knee osteoarthritis in detail

SummaryObjectiveRadiography is still the golden standard for imaging features of osteoarthritis (OA), such as joint space narrowing, subchondral sclerosis, and osteophyte formation. Objective assessment, however, remains difficult. The goal of the present study was to evaluate a novel digital method to analyse standard knee radiographs.MethodsStandardized radiographs of 20 healthy and 55 OA knees were taken in general practise according to the semi-flexed method by Buckland-Wright. Joint Space Width (JSW), osteophyte area, subchondral bone density, joint angle, and tibial eminence height were measured as continuous variables using newly developed Knee Images Digital Analysis (KIDA) software on a standard PC.Two observers evaluated the radiographs twice, each on two different occasions. The observers were blinded to the source of the radiographs and to their previous measurements. Statistical analysis to compare measurements within and between observers was performed according to Bland and Altman. Correlations between KIDA data and Kellgren & Lawrence (K&L) grade were calculated and data of healthy knees were compared to those of OA knees.ResultsIntra- and inter-observer variations for measurement of JSW, subchondral bone density, osteophytes, tibial eminence, and joint angle were small. Significant correlations were found between KIDA parameters and K&L grade. Furthermore, significant differences were found between healthy and OA knees.ConclusionIn addition to JSW measurement, objective evaluation of osteophyte formation and subchondral bone density is possible on standard radiographs. The measured differences between OA and healthy individuals suggest that KIDA allows detection of changes in time, although sensitivity to change has to be demonstrated in long-term follow-up studies

dGEMRIC as a tool for measuring changes in cartilage quality following high tibial osteotomy:A feasibility study

SummaryObjectiveThe high tibial osteotomy (HTO) is an effective strategy for treatment of painful medial compartment knee osteoarthritis. Effects on cartilage quality are largely unknown. Delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) enables non-invasive assessment of cartilage glycosaminoglycan content. This study aimed to evaluate if dGEMRIC could detect relevant changes in cartilage glycosaminoglycan content following HTO.DesignTen patients with medial compartment osteoarthritis underwent a dGEMRIC scan prior to HTO, and after bone healing and subsequent hardware removal. A dGEMRIC index (T1Gd) was used for changes in cartilage glycosaminoglycan content, a high T1Gd indicating a high glycosaminoglycan content and vice versa. Radiographic analysis included mechanical axis and tibial slope measurement. Clinical scores [Knee Osteoarthritis Outcome Scale (KOOS), visual analogue score (VAS) for pain, Knee Society Clinical Rating System (KSCRS)] before, 3 and 6 months after HTO and after hardware removal were correlated to T1Gd changes.ResultsOverall a trend towards a decreased T1Gd, despite HTO, was observed. Before and after HTO, lateral femoral condyle T1Gd was higher than medial femoral condyle (MFC) T1Gd and tibial cartilage T1Gd was higher than that of femoral cartilage (P < 0.001). The MFC had the lowest T1Gd before and after HTO. Clinical scores all improved significantly (P < 0.01), KOOS Symptoms and QOL were moderately related to changes in MFC T1Gd.ConclusionsdGEMRIC effectively detected differences in cartilage quality within knee compartments before and after HTO, but no changes due to HTO were detected. Hardware removal post-HTO seems essential for adequate T1Gd interpretation. T1Gd was correlated to improved clinical scores on a subscore level only. Longer follow-up after HTO may reveal lasting changes.ClinicalTrials.gov registration ID: NCT01269944

An intelligent interactive segmentation method for the joint space in osteoarthritic ankles

Clinical reality is full of complex images that cannot be segmented automatically with current computer vision technology, requiring intensive user intervention. In [1] and [2] we proposed a framework for the systematic development of intelligent interactive segmentation techniques that aim at repeatable and predictable results obtained via efficient interaction. In this paper we apply this framework to segment the joint space boundary of osteoarthritic ankles. The solution is based on a heterogeneous boundary representation implemented with a new piecewise deformable model. User intervention is necessary only when this model fails, being performed via specialized interactive tools. Results obtained by a non-medical user are presented, indicating improvement over the manual practice in terms of accuracy and repeatabilit

Supervised segmentation methods for the hippocampus in MR images

This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

Supervised segmentation methods for the hippocampus in MR images

This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)