Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

PURPOSE: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. METHODS: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. RESULTS: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. CONCLUSIONS: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

Simple finite element models for use in the design of therapeutic footwear.

Therapeutic footwear is frequently prescribed in cases of rheumatoid arthritis and diabetes to relieve or redistribute high plantar pressures in the region of the metatarsal heads. Few guidelines exist as to how these interventions should be designed and what effect such interventions actually have on the plantar pressure distribution. Finite element analysis has the potential to assist in the design process by refining a given intervention or identifying an optimal intervention without having to actually build and test each condition. However, complete and detailed foot models based on medical image segmentation have proven time consuming to build and computationally expensive to solve, hindering their utility in practice. Therefore, the goal of the current work was to determine if a simplified patient-specific model could be used to assist in the design of foot orthoses to reduce the plantar pressure in the metatarsal head region. The approach is illustrated by a case study of a diabetic patient experiencing high pressures and pain over the fifth metatarsal head. The simple foot model was initially calibrated by adjusting the individual loads on the metatarsals to approximate measured peak plantar pressure distributions in the barefoot condition to within 3%. This loading was used in various shod conditions to identify an effective orthosis. Model results for metatarsal pads were considerably higher than measured values but predictions for uniform surfaces were generally within 16% of measured values. The approach enabled virtual prototyping of the orthoses, identifying the most favorable approach to redistribute the patient's plantar pressures.

Optimization of nonlinear hyperelastic coefficients for foot tissues using a magnetic resonance imaging deformation experiment.

Accurate prediction of plantar shear stress and internal stress in the soft tissue layers of the foot using finite element models would provide valuable insight into the mechanical etiology of neuropathic foot ulcers. Accurate prediction of the internal stress distribution using finite element models requires that realistic descriptions of the material properties of the soft tissues are incorporated into the model. Our investigation focused on the creation of a novel three-dimensional (3D) finite element model of the forefoot with multiple soft tissue layers (skin, fat pad, and muscle) and the development of an inverse finite element procedure that would allow for the optimization of the nonlinear elastic coefficients used to define the material properties of the skin muscle and fat pad tissue layers of the forefoot based on a Ogden hyperelastic constitutive model. Optimization was achieved by comparing deformations predicted by finite element models to those measured during an experiment in which magnetic resonance imaging (MRI) images were acquired while the plantar surface forefoot was compressed. The optimization procedure was performed for both a model incorporating all three soft tissue layers and one in which all soft tissue layers were modeled as a single layer. The results indicated that the inclusion of multiple tissue layers affected the deformation and stresses predicted by the model. Sensitivity analysis performed on the optimized coefficients indicated that small changes in the coefficient values (±10%) can have rather large impacts on the predicted nominal strain (differences up to 14%) in a given tissue layer.

Effect of material properties on predicted vesical pressure during a cough in a simplified computational model of the bladder and urethra.

Stress urinary incontinence is a condition that affects mainly women and is characterized by the involuntary loss of urine in conjunction with an increase in abdominal pressure but in the absence of a bladder contraction. In spite of the large number of women affected by this condition, little is known regarding the mechanics associated with the maintenance of continence in women. Urodynamic measurements of the pressure acting on the bladder and the pressures developed within the bladder and the urethra offer a potential starting point for constructing computational models of the bladder and urethra during stress events. The measured pressures can be utilized in these models to provide information to specify loads and validate the models. The main goals of this study were to investigate the feasibility of incorporating human urodynamic pressure data into a computational model of the bladder and the urethra during a cough and determine if the resulting model could be validated through comparison of predicted and measured vesical pressure. The results of this study indicated that simplified models can predict vesical pressures that differ by less than 5 cmH(2)O (<10%) compared to urodynamic pressure measurements. In addition, varying material properties had a minimal impact on the vesical pressure and displacements predicted by the model. The latter finding limits the use of vesical pressure as a validation criterion since different parameters can yield similar results in the same model. However, the insensitivity of vesical pressure predictions to material properties ensures that the outcome of our models is not highly sensitive to tissue material properties, which are not well characterized.

Modeling physiology of the urinary tract.

We review mathematical and physical models of physiology of the organs of the urinary tract and their functions of producing, excreting, and voiding urine. Models for urine concentration in the kidney, urine flow in the ureters, bladder filling and emptying, urethral function during micturition, pelvic floor muscles, and neural control are reviewed in the context of their application to the development of new diagnostic and therapeutic techniques. The focus of this review is on modeling of physiology and function at the tissue and organ level, as almost all research to date has been done in those areas. Although physiological models of the lower urinary tract are in their infancy, they have the long-term potential to improve our understanding of physiological mechanisms, as well as to provide environments for simulation or testing in silico of new therapies and techniques.