The estimation of the optical flow between two images is one of the key problems in low-level vision. According the optical flow evaluation site at http://vision.middlebury.edu/flow/, discontinuity preserving variational models based on Total Variation (TV) regularization and L 1 data terms are among the most accurate flow estimation techniques, but there is still room for improvements.

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Accurate techniques for simulating the deformation of soft biological tissues are an increasingly valuable tool in many areas of biomechanical analysis and medical image computing. To model the complex morphology and response of articular cartilage, a hyperviscoelastic (dispersed) fiber-reinforced constitutive model is employed to complete two specimen-specific finite element (FE) simulations of an indentation experiment, with and without considering fiber dispersion. Ultra-high field Diffusion Tensor Magnetic Resonance Imaging (17.6 T DT-MRI) is performed on a specimen of human articular cartilage before and after indentation to approximately 20% compression. Based on this DT-MRI data, we detail a novel FE approach to determine the geometry (edge detection from first eigenvalue), the meshing (semi-automated smoothing of DTI measurement voxels), and the fiber structural input (estimated principal fiber direction and dispersion). The global and fiber fabric deformations of both the un-dispersed and dispersed fiber models provide a satisfactory match to that estimated experimentally. In both simulations, the fiber fabric in the superficial and middle zones becomes more aligned with the articular surface, although the dispersed model appears more consistent with the literature. In the future, a multi-disciplinary combination of DT-MRI and numerical simulation will allow the functional state of articular cartilage to be determined in vivo.

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(2)=0.95+/-0.03, mean+/-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+/-25% (M+/-SD). The correlation coefficients across all predictions have a minimum of 0.939, and a maximum of 0.993 with 0.975+/-0.013 (M+/-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.

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