a b s t r a c tAs the presence of finite element implementations on General Purpose Graphics Processing Units (GPGPUs) is the literature increases, detailed and in-breadth testing of the hardware is somewhat lacking. We present an implementation and detailed analysis of an FE algorithm designed for real-time solution, particularly aimed at elasticity problems applied to soft tissue deformation.An efficient parallel implementation of Total Lagrangian Explicit Dynamics implementation is elucidated and the potential for real-time execution is examined. It is shown that in conjunction with modern computing architectures, solution times can be significantly reduced, depending on the solution strategy.The usability of the method is investigated by conducting a broad assay on ranging model sizes and different cards and comparing to an industry-proven FE code Abaqus. In doing so, we study the effect of using single/double precision computation, quantify and present error measurements as a function of the number of time-steps. We also examine the usage of a special texture memory space and its effect on computation for different devices. Adding material complexity in the form of a tissue damage model is presented and its computational impact elucidated. The aggregate results show that, for a particular set of problems, it is possible to compute a simple set of test cases 30-120 times faster than current commercial solutions.According to the speedups achieved, an indication is provided that the GPGPU technology shows promise in the undertaking of real-time FE computation.
Accurate estimation of peak wall stress (PWS) is the crux of biomechanically motivated rupture risk assessment for abdominal aortic aneurysms aimed to improve clinical outcomes. Such assessments often use the finite element (FE) method to obtain PWS, albeit at a high computational cost, motivating simplifications in material or element formulations. These simplifications, while useful, come at a cost of reliability and accuracy. We achieve research-standard accuracy and maintain clinically applicable speeds by using novel computational technologies. We present a solution using our custom finite element code based on graphics processing unit (GPU) technology that is able to account for added complexities involved with more physiologically relevant solutions, e.g. strong anisotropy and heterogeneity. We present solutions up to 17x faster relative to an established finite element code using state-of-the-art nonlinear, anisotropic and nearly-incompressible material descriptions. We show a realistic assessment of the explicit GPU FE approach by using complex problem geometry, biofidelic material law, doubleprecision floating point computation and full element integration. Due to the increased solution speed without loss of accuracy, shown on five clinical cases of abdominal aortic aneurysms, the method shows promise for clinical use in determining rupture risk of abdominal aortic aneurysms.
Finite element (FE) simulations are increasingly valuable in assessing and improving the performance of biomedical devices and procedures. Due to high computational demands such simulations may become difficult or even infeasible, especially when considering nearly incompressible and anisotropic material models prevalent in analyses of soft tissues. Implementations of GPGPU-based explicit FEs predominantly cover isotropic materials, e.g. the neo-Hookean model. To elucidate the computational expense of anisotropic materials, we implement the Gasser-Ogden-Holzapfel dispersed, fiber-reinforced model and compare solution times against the neo-Hookean model. Implementations of GPGPU-based explicit FEs conventionally rely on single-point (under) integration. To elucidate the expense of full and selective-reduced integration (more reliable) we implement both and compare corresponding solution times against those generated using underintegration. To better understand the advancement of hardware, we compare results generated using representative Nvidia GPGPUs from three recent generations: Fermi (C2075), Kepler (K20c), and Maxwell (GTX980). We explore scaling by solving the same boundary value problem (an extension-inflation test on a segment of human aorta) with progressively larger FE meshes. Our results demonstrate substantial improvements in simulation speeds relative to two benchmark FE codes (up to 300[Formula: see text] while maintaining accuracy), and thus open many avenues to novel applications in biomechanics and medicine.
Advances in miniaturized surgical instrumentation are key to less demanding and safer medical interventions. In cardiovascular procedures interventionalists turn towards catheter-based interventions, treating patients considered unfit for more invasive approaches. A positive outcome is not guaranteed. The risk for calcium dislodgement, tissue damage or even vessel rupture cannot be eliminated when instruments are maneuvered through fragile and diseased vessels. This paper reports on the progress made in terms of catheter design, vessel reconstruction, catheter shape modeling, surgical skill analysis, decision-making and control. These efforts are geared towards the development of the necessary technology to autonomously steer catheters through the vasculature, a target of the EU-funded project CASCADE (Cognitive AutonomouS CAtheters operating in Dynamic Environments). Whereas autonomous placement of an aortic valve implant forms the ultimate and concrete goal, the technology of individual building blocks to reach such ambitious goal is expected to be much sooner impacting and assisting interventionalists in their daily clinical practice.
The Command and Control Reference Model is a layered structure interacting with the environment through four physical ports: Communications, Sensing/Monitoring, Infliction and Transportation. An Object Oriented Stochastic C2 Graph architecture has been developed to model the combat and C2 dynamics of the associated processes within different layers. The latter structure must interact with a tool which models communications networks in a hierarchical layered fashion. The PlanystTM program has been developed by IRI Computer Communications Corporation to serve as such a tool. Its features are described in this paper. 1.RI's Planyst is a unique modeling and analysis tool for communication network systems. It employs a powerful graphical user interface which allows the user to define the network configuration through the placement of icons on the screen. By clicking on these icons, and through the use of hierarchically structured menus, the user is able to quickly define the parameters of the network configuration under investigation.The Planyst tool can be effectively employed in support of modeling, analysis and planning of command and control systems and networks. In accordance with the C2 Reference Model Ell, command and control systems are modeled in terms of a layered structure which consists of the following (stated from higher to lower) layers: -C2 Conflict layer, whose product is the mission -C2 Presentation (Planning) layer, where the plans for achieving the stated mission are developed -C2 Operation layer, wherein tasks are developed to achieve the plans -C2 Procedure layer, where existing modes of operation (reinforced through training and experience) are used, defining jobs to be carried out for the execution of the plans -C2 Network layer, where job assignments are distributed over multiple entities in accordance with the underlying topology -C2 Link layer, which defined a transaction across a single link as the basic entity required to realize the network assignment. -C2 Asset layer, where the C2 resource is modeled. Interactions between assets are divided into 4 categories The C2 Reference Model and C2 Analysis Tools * PlanystTM is a Trade Mark of IRI Computer Communications Corporation, 19562 Ventura Blvd., Suite #209, Tarzana, CA. 91356, Attn.: Dr. Izhak Rubin. Tel.: (818)996-9805. (so that four interaction ports connect from below to the C2 Asset layer, providing the latter with the corresponding interaction services): a. Communications interactions. The communications process itself is modeled through the use of the Open System Interconnect (OSI) Communications Reference Model. The latter is a layered model. b. Identification (sensing) interactions. c. Infliction interactions. d. Transportation interactions. Integrated resource allocation, C2 and combat analysis tools have been developed [1]-[6] based upon the C2 reference model. The latter models use stochastic process methodologies to describe and analyze the dynamics of the combat processes and the associated resource allocation schemes, across ...
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