Sim City: The value of simulation
It’s important to recognise value of simulation, say Jessica James and Rebekah Dixon of the Synopsys Simpleware Product Group.
Patient-specific modelling provides a number of opportunities and challenges for medical device design. Using the image data from MRI and CT scans to build the models enables anatomical accuracy, capturing each patients’ unique features. This image data can then be used during the design stage for medical devices to integrate CAD models of components with anatomies and simulate performance. When used to complement experimental testing, simulation using techniques like finite element analysis (FEA) increases choice when optimising and validating design decisions.
Computational modelling of this kind is increasingly being recognised by medical device manufacturers and healthcare regulators. These methods also tie into industry 4.0 trends for smart manufacturing, partial automation of tasks like pre-surgical planning, technological customisation, and improved human and technology interactions. However, there remains some key hurdles for using image-based modelling, including, accessibility for learning software, data availability, and avoiding obsolescence.
Future proofing anatomical simulations
Several solutions are in place for improving decision-making at the design stage for medical devices, with the goal of reducing risk and making it easier to tailor new technologies to improve the quality of the final implant. These approaches are particularly aimed at increasing understanding of human-implant design, where patient-specific optimisation of hip implants, pacemakers, stents, and other devices can improve outcomes.
For example, Simpleware ScanIP from Synopsys is a well-established software that converts image data taken from MRI and CT into 3D models. These are suitable for examining how devices interact with the human body. Image processing tools enable segmentation of anatomical regions of interest, measurement and statistical quantification of anatomies, and the integration of CAD-designed implants. The benefit of using these virtual models is that multiple device designs can be studied to decide on optimal placement, as well as for understanding key factors like wear and movement after surgery.
A key push in software development is to reduce the number of workflow steps needed between patient scan and completed model, as well as to improve communications between different teams through the manufacturing lifecycle. One such workflow is to use Simpleware software to create a 3D model from image data and export it directly to a simulation tool for carrying out FEA of how the implant will perform within the body.
An important part of this process is to ‘future-proof’ the technology to prevent obsolescence and apply trusted techniques to new patient datasets and more powerful simulations. This process can be scaled up to account for multiple implant designs and simulation requirements. For medical device designers, routes from 3D images to models can be extended to exporting files for 3D printing of implants, suitable for visualising and planning procedures.
Automating pre-surgical workflows
One of the more recent innovations in medical device design has involved semi-automating pre-clinical guides to quickly and accurately deliver models to surgeons and other medical professionals. For example, Corin Group in Australia have developed an Optimised Positioning System (OPS) technology that uses Simpleware software tools to help plan total hip replacements. 3D scans of patients are obtained from CT data and X-ray tomography, and imported to Simpleware software to segment and incorporate CAD-designed implant models. Corin Group’s OPS tool then analysed different types of movement to understand the optimal position of cups, and translate the results into 3D printed surgical guides. Scripting tools are used to speed up this process, with the end-goal of improving the options available to clinicians when planning an operation.
Conclusions
The future of medical device technology and the design of better implants will depend on the availability, quality, and reliability of software solutions. Patient-specific planning from imaging data offers multiple strengths for researchers and clinicians, including being able to tailor treatment to individual anatomies. Simulation allows researchers and medical device manufacturers to carry out more exhaustive testing of designs before they reach the market, reducing the risk of later failures which may have significant impact on the patient’s health.