Faster, More Accurate Blood Flow Simulation to Revolutionize Treatment of Vascular Diseases

The research team, led by The University of Manchester (Manchester, UK), evaluated cutting-edge techniques that make the simulation process faster but still maintain the required accuracy for such important applications. They discovered that Reduced Order Modeling (ROM), which simplifies computational complexity, can be selectively applied to accurately hasten different types of vascular flow modeling. Additionally, they found that Machine Learning could address limitations in ROM or even introduce completely new simulation methods applicable to a broad range of vascular flow modeling challenges. These developments could be transformative for the field of vascular medicine.

Moreover, this research underscores the importance of these accelerated simulation methods for in-silico trials. These virtual simulations are crucial in developing and gaining regulatory approval for new medical devices. By employing these accelerated techniques, in-silico trials can be carried out with a level of speed and precision previously unattainable, potentially eliminating the need for traditional clinical trials, which are often costly and time-consuming. The research also calls for a collective effort to create a benchmarking framework for simulation acceleration methods. Such a framework would provide standardized metrics to measure accuracy and efficiency across different simulation techniques, fostering transparency and comparability in this rapidly evolving area.

“The potential ramifications are immense - from the development of enhanced medical devices that can be custom-fitted to individual patient anatomy, to providing real-time insights during surgical procedures, advancements in these techniques could improve patient outcomes and the standard of care,” said Professor Alex Frangi, Bicentennial Turing Chair in Computational Medicine.

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