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Background: Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA). Purpose: To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA. Study Type: Retrospective study. Subjects: Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren‐Lawrence [KL] grade: 0; final KL grade: 3 over the 10‐year study). Field Strength/Sequence: 3.0‐T and 14.1‐T, biomechanics‐based displacement‐encoded imaging, fast spin echo, multi‐slice multi‐echo T2 mapping. Assessment: We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades. Statistical Tests: Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold at α = 0.05. Results: Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R2 = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not. Data Conclusion: Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early‐stage degeneration. Evidence Level: 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

To model the cartilage morphology and the material response, a phenomenological and patient-specific simulation approach incorporating the collagen fiber fabric is proposed. Cartilage tissue response is nearly isochoric and time-dependent under physiological pressure levels. Hence, a viscoelastic constitutive model capable of reproducing finite strains is employed, while the time-dependent deformation change is purely isochoric. The model incorporates seven material parameters, which all have a physical interpretation. To calibrate the model and facilitate further analysis, five human cartilage specimens underwent a number of tests. A series of magnetic resonance imaging (MRI) sequences is taken, next the cartilage surface is imaged, then mechanical indentation tests are completed at 2-7 different locations per sample, resulting in force-displacement data over time, and finally, the underlying bone surface is imaged. Imaging and mechanical testing are performed with a custom-built robotics-based testing device. Stereo reconstruction of the cartilage and subchondral bone surface is employed, which, together with the proposed constitutive model, led to specimen-specific finite element simulations of the mechanical indentation tests. The force-time response of 23 such indentation experiment simulations is optimized to estimate the mean material parameters and corresponding standard deviations. The model is capable of reproducing the deformation behavior of human articular cartilage in the physiological loading domain, as demonstrated by the good agreement between the experiment and numerical results ([R.sup.2] = 0.95 [+ or -] 0.03, mean [+ or -] standard deviation of force-time response for 23 indentation tests). To address validation, a sevenfold cross-validation experiment is performed on the 21 experiments representing healthy cartilage. To quantify the predictive error, the mean of the absolute force differences and Pearson's correlation coefficient are both calculated. Deviations in the mean absolute difference, normalized by the peak force, range from 4% to 90%, with 40 [+ or -] 25% (M [+ or -] SD). The correlation coefficients across all predictions have a minimum of 0.939, and a maximum of 0.993 with 0.975 [+ or -] 0.013 (M [+ or -] SD), which demonstrates an excellent match of the decay characteristics. A novel feature of the proposed method is 3D sample-specific numerical tracking of the fiber fabric deformation under general loading. This feature is demonstrated by comparing the estimated fiber fabric deformation with recently published experimental data determined by diffusion tensor MRI. The proposed approach is efficient enough to enable large-scale 3D contact simulations of knee joint loading in simulations with accurate joint geometries. [DOI: 10.1115/1.3148471] Keywords: articular cartilage, collagen fibers, finite viscoelasticity, finite element simulation, indentation testing

The ubiquitous eutectic tin-lead (Sn-Pb) solder alloys are soon to be replaced with lead-free alternatives. In light of this transition, new computational tools for predicting the fatigue life of lead-free solders are required. A fatigue life prediction methodology was developed, based on stress-strain, creep, and isothermal fatigue data; the latter generated using a double lap-shear (DLS) test assembly. The proposed fatigue life prediction methodology builds on current practices in fatigue prediction for solder alloys, particularly the concepts of unpartitioned energy methods in finite element analysis (FEA) and continuum damage mechanics. As such, the current state of these fields is briefly discussed. Next, the global and local FEA simulations of the DLS test assembly are detailed. A correlation is then made between the empirical data and the FEA simulations. A general fatigue life prediction methodology is next described in detail. Finally, this methodology is tested and verified against the empirical data. [DOI: 10.1115/1.3068313]