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Titlebook: Computational Biomechanics for Medicine; Imaging, Modeling an Grand R. Joldes,Barry Doyle,Karol Miller Conference proceedings 2016 Springer

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Structure Formation in Solution with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.
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Robust Landmark Identification for Generating Subject Specific Models for Biomechanicsle to identify the position of the tracheal bifurcation and jugular notch landmarks with a mean ± SD error of 6.1 ± 5.2 mm, with 9.1 % of the errors greater than 10 mm. This result was three times better than the standard template matching method, which gave a mean ± SD error of 18.9 ± 21.7 mm, with 33 % of the errors greater than 10 mm.
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Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Mode with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.
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Computational Simulation of Blood Flow and Drug Transportation in a Large Vasculaturedrug transportation in the vasculature. Thirdly, we model the delivery of the drug into a tiny tissue block by adopting a 3D diffusion equation. In conclusion the presented computational techniques constitute a pipeline for circulation modelling in multiple scales, and may be used in a variety of biomedical applications.
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https://doi.org/10.1007/3-540-27715-3metric center. In presence of a single scattering inhomogeneity PDNM moves toward the center of it. Diffuse transmittance intensity decay in undeformed objects is 8–10 % faster than in those with deformations.
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