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Objective A deformable registration technique was developed and evaluated to track and quantify tumor response to radiofrequency ablation for patients with liver malignancies. Materials and methods The method uses the combined power of global and local alignment of pre- and post-treatment computed tomography image data sets. The strategy of the algorithm is to infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). Using this framework, the major challenge for tracking tumor location is not the tissue mechanical properties for FEM modeling but rather the evaluation of boundary conditions. Three different methods were systematically investigated to automatically determine the boundary conditions defined by the correspondences on liver surfaces. Results Using both 2D synthetic phantoms and imaged 3D beef liver data we performed gold standard registration while measuring the accuracy of non-rigid deformation. The fact that the algorithms could support mean displacement error of tumor deformation up to 2 mm indicates that this technique may serve as a useful tool for surgical interventions. The method was further demonstrated and evaluated using consecutive imaging studies for three liver cancer patients. Conclusion The FEM-based surface registration technique provides accurate tracking and monitoring of tumor and surrounding tissue during the course of treatment and follow-up.

While significant advances have come about for turbomachinery off-design performance characterization using computational fluid dynamics (CFD), the need for quick performance estimates at challenging off-design conditions still requires the use of lower-order models, such as mean-line analyses and through-flow tools. These inviscid tools require blade performance correlations formulated in terms of loss and turning angle as a function of blade geometric and aerodynamic parameters. Traditionally, such correlations have relied on the empirical data from blade cascade tests at nominal incidence conditions. This limitation on the applicability of the blade correlations has caused performance modeling of the sub-idle regime to be off-limits to this type of correlation-based approaches. This paper addresses the development of blade loss and deviation models applicable to the sub-idle regime using a parametric numerical approach. 2D CFD results are used to generate a model that is then applied to mean-line and through-flow analyses aimed at predicting the sub-idle map of an axial flow compressor. The model proves to be a valuable tool for quick sub-idle performance estimates and allows existing correlation-based performance prediction methods to be extended into the sub-idle regime.