Emerging Trends: Where Could PCM&S Go?
As the use of simulation continues to expand, there are a few areas emerging as topics that may become increasingly significant in extending the implementation of simulation driven design and manufacturing. A selection of a few of these areas is provided in the remainder of this article.
6.1: Regulatory viewpoint
The FDA is actively encouraging the use of PCM&S to support the development and use of medical products and technology for public health benefits. In parallel, the Senate Appropriations Committee is openly encouraging the FDA to “…. explore greater use, where appropriate, of in silico trials for advancing new devices and drug therapy applications”. Aspects of in vitro and in vivo trials are now being actively replaced by in silico trials using computational simulations as a critical component in the development of new devices and patient therapies. With the increased insight into the operation and limitations of the effectiveness of medical device technology, a greater understanding of the increasingly complex behavior and interactions of medical device technology with human anatomy and physiology is being obtained, thus limiting potential risk to the public.
In addition, the development of life-saving medical technology can be made increasingly effective while simultaneously cutting the time and cost of the development process. Some Federal studies suggest that technology development using PCM&S can reduce development time by up to 90% and concurrently reduce the cost for development by up to 50%. Assessment of the benefits of PCM&S for a medical device development has documented device release up to 2 years earlier than scheduled with a resulting increase in the number of patients receiving benefit of up to 10,000. Reductions in the number of patients required to participate in the clinical trial led to overall cost savings approaching $10 million.
6.2: Human anatomy and physiology effects
In general, PCM&S has the advantage that a causal relation between model input and output can be established, whereas animal testing and human clinical trials can only provide a statistical correlation. Through the creation of virtual patient anatomies that account for variability in anatomy and physiology or in the use condition and/or product performance, virtual equivalents of in vivo clinical trials can be constructed. The use of such individualized computer simulations in the development or regulatory evaluation of a medicinal product, device, or intervention can allow simulated clinical trials. While assessment of all necessary endpoints is not feasible with the current understanding of biology, in silico clinical trials have successfully been used to reduce the complexity, size and duration of in vivo clinical trials.
To promote the assessment of the effect of human anatomy and physiology on the operation and safety of medical devices and technology, the FDA has collaborated with European organizations to develop “The Virtual Family”: a set of four highly detailed, anatomically correct whole-body models of an adult male, an adult female, and two children (1). In parallel, Duke University has developed 54 adult models (male and female) of varying body types and ages as well as 58 pediatric models from 2 to 18 years of age (2). The four models are based on high-resolution magnetic resonance imaging (MRI) data of healthy volunteers. Organs and tissues of are represented by three-dimensional, highly detailed CAD. Currently, The Virtual Family models have been used for electromagnetic, thermal, acoustic and computational fluid dynamics (CFD) simulations.
Electromagnetic and thermal simulations have been performed to assess the safety of active and passive implanted devices on whole-body MRI coils (3). Electromagnetic and CFD simulations have calculated the magneto-hemodynamic effect as a biomarker for cardiac output (4). Acoustic simulations have been performed to assess the impact of the human anatomy on the propagation ultrasound waves (5). At the end of 2014, The Virtual Family was used to support over 120 medical device submissions to FDA.
6.3: Quantifying uncertainty
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real-world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. This replaces traditional deterministic approaches with probabilistic solutions that can quantify the degree of risk that may be present. For example, physical data may be missing or unavailable. We may traditionally assume that material failure occurs at a single value of load, in practice there is a statistical distribution of failure loads. Physical testing may introduce errors due to variations and uncertainty in measurements. Uncertainty may also be introduced by approximations made in setting up the simulation from sources such as physical simulation errors due to inaccuracies in the mathematical representation of the phenomenon, errors in representing the precise geometry of the system, discretization and solution errors, and numerical round off errors.
Quantification of levels of uncertainty is now available and can influence how the results of simulation can be best interpreted and how to best use approaches for simulation in any subsequent attempt to develop optimal solutions and inverse problem solving.
6.4: Developing optimal solutions
One of the most valuable aspects of simulation is the ability to characterize the response of arbitrarily complex stochastic systems. Once a system has been successfully simulated, the simulations provide information about the response to select input conditions. Simulations in which parameters of interest are swept over a range of interest to the user while other parameters remain constant, allow the effect of the parameters on the performance objective to be calculated. While informative, this approach is a time-consuming method and may provide limited understanding and only partially improves the performance. To obtain an optimal solution with minimum computation and time, the problem must be solved iteratively where in each iteration the solution moves closer to the optimum solution.
In contrast, optimization processes select the best possible decision for a given set of circumstances without having to enumerate all of the possibilities. Simulation-based optimization methods are generally performed by two different techniques: Derivative-Free and Gradient-Based Optimization. Derivative-Free Optimization (DFO) is useful when the objective functions and constraints may be discontinuous and do not have analytic derivatives and have the advantage of simplicity. DFO requires less user interaction to set up but, due to the computational costs, are most effective when the number of design variables is around 10 or less.
Gradient Based Optimization (GBO) approaches looks for the local minimum of a desired objective function. The advantage of the gradient-based method is that it can address problems involving hundreds, or even thousands, of design variables with very low increase in computational cost as the number of design variables increases.
Optimization methods can be further classified by the types of variables being optimized: Dimension, Shape, and Topology.
- Dimensional optimization involves defining design variables and is usually used as the last step in the design process. It is performed once the design is more or less fixed in terms of the overall shape and typically incorporates DFO methods
- Shape optimization typically occurs earlier in the design process, and involves a more free-form alteration of the object. More care is usually required for choosing the design variables, as the objective is to improve the shape without over-constraining the design. For shape optimization the gradient-based method is preferred if an analytic objective function can be found.
- Topology optimization is used very early in the design process, typically in the conceptual stage. Topology optimization treats the distribution of material as a design variable and inserts or removes structures to improve the objective function. Due to the high number of design variables, GBO approaches are incorporated.
6.5: Expanding the use of simulation
Historically, simulation has required the use of experienced personnel, powerful software and dedicated hardware. Recent developments have moved in the direction of putting dedicated simulation tools in the hands of scientists and engineers who have no direct experience or knowledge of simulation. These SimApps essentially take the underlying computational simulation file but provide an easy-to-use GUI through which the inexperienced user controls predefined inputs. These are used to automatically perform the required simulation and display critical results. While SimApps are generally for solutions to a highly focused set of predefined problems, the range of potential applications covers the full range of physics and use of medical devices.
Currently Sim Apps are being adopted by a range of companies to study product design and manufacturing processing. However, most recently SimApps are being developed to support adoption of new therapies for cancer treatment. These SimApps are designed to be used by practicing clinicians to develop patient specific treatment protocols. In this way, new therapies can be brought to the patient application effectively and quickly.
Conclusion
- PCM&S is being widely used to aid the demonstration, development and manufacture of medical products.
- Increased adoption is driven by the increase in the number of different physical phenomena that can now be included in a simulation and the ability to include human body responses and structure into simulations.
- Simulation can provide significant reductions in expense and time for product development compared to traditional testing and evaluation approaches, with reported benefits showing up to a 90% reduction in time and simultaneously a 50% reduction in cost.
- Regulatory bodies are accepting the use of simulation data to support approval for use of new devices and, in some cases, actively requiring the use of simulation in supporting documentation.
References
- Christ A., Kainz W., Hahn E.G., Honegger K., Zefferer M., Neufeld E., Rascher W., Janka R., Bautz W., Chen J., Kiefer B., Schmitt P., Hollenbach H.P., Shen J.X., Oberle M., and Kuster N., “The Virtual Family – Development of Anatomical CAD Models of two Adults and two Children for Dosimetric Simulations”, Physics in Medicine and Biology, 55, N23–N38, 2010
- XCAT Anatomy Files – CVIT – Center for Virtual Imaging Trials (duke.edu)
- Neufeld E., Gosselin M.-C., Murbach M., Christ A., Cabot E. and Kuster N., “Analysis of the local worst-case SAR exposure caused by an MRI multi-transmit body coil in anatomical models of the human body”, Phys. Med. Biol. 56, 4649–4659, 2011
- Kyriakou A., Neufeld E., Szczerba D., Kainz W., Luechinger R., Kozerke S., McGregor R., Kuster N., “Patient-specific simulations and measurements of the magneto-hemodynamic effect in human primary vessels”, Physiol. Meas., 33(2):117-30, 2012
- Kyriakou, A., Neufeld, E., Werner, B., Paulides, M., Szekely, G. & Kuster, N., “A Review of Numerical and Experimental Compensation Techniques for Skull-Induced Phase Aberrations in Transcranial Focused Ultrasound”, International Journal of Hyperthermia, 30(1):36-46, 2014