Correlation of Patient Symptoms With Labral and Articular Cartilage Damage in Femoroacetabular Impingement.

BackgroundFemoroacetabular impingement (FAI) can lead to labral and articular cartilage injuries as well as early osteoarthritis of the hip. Currently, the association of patient symptoms with the progression of labral and articular cartilage injuries due to FAI is poorly understood.PurposeTo evaluate the correlation between patient-reported outcome (PRO) scores and cartilage compositional changes seen on quantitative magnetic resonance imaging (MRI) as well as cartilage and labral damage seen during arthroscopic surgery in patients with FAI.Study designCohort study; Level of evidence, 3.MethodsPatients were prospectively enrolled before hip arthroscopic surgery for symptomatic FAI. Patients were included if they had cam-type FAI without radiographic arthritis. All patients completed PRO scores, including the Hip disability and Osteoarthritis Outcome Score (HOOS) and a visual analog scale for pain. MRI with mapping sequences (T1ρ and T2) on both the acetabular and femoral regions was performed before surgery to quantitatively assess the cartilage composition. During arthroscopic surgery, cartilage and labral injury grades were recorded using the Beck classification. Pearson and Spearman correlation coefficients were then obtained to evaluate the association between chondrolabral changes and PRO scores.ResultsA total of 46 patients (46 hips) were included for analysis (mean age, 35.5 years; mean body mass index [BMI], 23.9 kg/m2; 59% male). Increasing BMI was correlated with a more severe acetabular cartilage grade (ρ = 0.37; 95% CI, 0.08-0.65). A greater alpha angle was correlated with an increased labral tear grade (ρ = 0.59; 95% CI, 0.37-0.82) and acetabular cartilage injuries (ρ = 0.61; 95% CI, 0.42-0.80). With respect to PRO scores, increasing femoral cartilage damage in the anterosuperior femoral head region, as measured on quantitative MRI using T1ρ and T2 mapping, correlated with lower (worse) scores on the HOOS Activities of Daily Living (r = 0.35; 95% CI, 0.06-0.64), Symptoms (r = 0.32; 95% CI, 0.06-0.57), and Pain (r = 0.31; 95% CI, 0.06-0.55) subscales. There was no correlation between PRO scores and acetabular cartilage damage or labral tearing found on quantitative MRI or during arthroscopic surgery.ConclusionFemoral cartilage damage, as measured on T1ρ and T2 mapping, appears to have a greater correlation with clinical symptoms than acetabular cartilage damage or labral tears in patients with symptomatic FAI

Deep learning predicts total knee replacement from magnetic resonance images

Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling ''normal'' post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC $0.834 \pm 0.036$ (p < 0.05). Most notably, the pipeline predicts TKR with AUC $0.943 \pm 0.057$ (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset.Comment: 18 pages, 5 figures (4 in main article, 1 supplemental), 8 tables (5 in main article, 3 supplemental). Submitted to Scientific Reports and currently in revisio

Age- and Gender-Related Differences in the Geometric Properties and Biomechanical Significance of Intracortical Porosity in the Distal Radius and Tibia

Cortical bone contributes the majority of overall bone mass and bears the bulk of axial loads in the peripheral skeleton. Bone metabolic disorders often are manifested by cortical microstructural changes via osteonal remodeling and endocortical trabecularization. The goal of this study was to characterize intracortical porosity in a cross-sectional patient cohort using novel quantitative computational methods applied to high-resolution peripheral quantitative computed tomography (HR-pQCT) images of the distal radius and tibia. The distal radius and tibia of 151 subjects (57 male, 94 female; 47 ± 16 years of age, range 20 to 78 years) were imaged using HR-pQCT. Intracortical porosity (Ct.Po) was calculated as the pore volume normalized by the sum of the pore and cortical bone volume. Micro–finite element analysis (µFE) was used to simulate 1% uniaxial compression for two scenarios per data set: (1) the original structure and (2) the structure with intracortical porosity artificially occluded. Differential biomechanical indices for stiffness (ΔK), modulus (ΔE), failure load (ΔF), and cortical load fraction (ΔCt.LF) were calculated as the difference between original and occluded values. Regression analysis revealed that cortical porosity, as depicted by HR-pQCT, exhibited moderate but significant age-related dependence for both male and female cohorts (radius ρ = 0.7; tibia ρ = 0.5; p < .001). In contrast, standard cortical metrics (Ct.Th, Ct.Ar, and Ct.vBMD) were more weakly correlated or not significantly correlated with age in this population. Furthermore, differential µFE analysis revealed that the biomechanical deficit (ΔK) associated with cortical porosity was significantly higher for postmenopausal women than for premenopausal women (p < .001). Finally, porosity-related measures provided the only significant decade-wise discrimination in the radius for females in their fifties versus females in their sixties (p < .01). Several important conclusions can be drawn from these results. Age-related differences in cortical porosity, as detected by HR-pQCT, are more pronounced than differences in standard cortical metrics. The biomechanical significance of these structural differences increases with age for men and women and provides discriminatory information for menopause-related bone quality effects. © 2010 American Society for Bone and Mineral Research

Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls

In the current study, our purpose is to evaluate the feasibility of applying deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in healthy controls scanned at 0.55T and compared with 3.0T. The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0.55T, both qualitatively and quantitatively, in terms of comparing segmentation performance, areas of improvement, and compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial results demonstrate a usable to good technical feasibility of translating existing quantitative deep-learning-based image segmentation techniques, trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition environment. Especially in terms of segmenting cartilage compartments, the models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be utilized for evaluating knee cartilage thickness and bone segmentations aided by established DL algorithms trained at higher-field strengths out-of-the-box initially. This could be utilized at the far-spread point-of-care locations with a lack of radiologists available to manually segment low-field images, at least till a decent base of low-field data pool is collated. With further fine-tuning with manual labeling of low-field data or utilizing synthesized higher SNR images from low-field images, OA biomarker quantification performance is potentially guaranteed to be further improved.Comment: 11 Pages, 3 Figures, 2 Table

X-ray detection of structural orientation in human articular cartilage

AbstractObjective: To determine the feasibility of detecting the structural orientation in cartilage with Diffraction Enhanced X-Ray Imaging.Design: Human tali and femoral head specimens were Diffraction Enhanced X-Ray Imaged (DEI) at the SYRMEP beamline at Elettra at various energy levels to detect the architectural arrangement of collagen within cartilage. DEI utilizes a monochromatic and highly collimated beam, with an analyzer crystal that selectively weights out photons according to the angle they have been deviated with respect to the original direction. This provides images of very high contrast, and with the rejection of X-ray scatter.Results: DEI allowed the visualization of articular cartilage and a structural orientation, resembling arcades, within.Conclusion: Our diffraction enhanced images represent the first radiographic detection of the structural orientation in cartilage. Our data are in line with previous studies on the structural organization of joint cartilage. They confirm the model of a vaulting system of collagen fiber bundles interrupted by proteoglycan aggregates