Acceleration of Multi-Scale, Multi-Physics Computational Models of the Human Heart using Machine Learning Computational models of the heart are poised to assist
clinical decision making, from triage (e.g. non-invasive
fractional flow reserve) to individualized therapy
selection (e.g. cardiac resynchronization therapy) to
intervention guidance (e.g. ablation therapy of cardiac
arrhythmias). These models simulate the complex, multi-
scale biophysics involved in cardiac function.
Siemens Healthineers has developed a unique, end-to-end
technology stack to go from medical data to virtual hearts
that includes anatomical, electrophysiological,
biomechanical and fluid-dynamics modeling. Although already
fast, a speed up of one or more order of magnitude is still
needed to bring this technology to the bedside: first, to
be able to estimate the virtual heart model from medical
data in minutes and not hours, as it currently does due to
the large number of simulations over several heart beats
needed for parameter estimation; and second to allow real-
time, model-based guidance during interventions.
The objective of this work is to explore AI-based
approaches to enable extremely-fast but accurate
biophysical simulations. Building upon the universal
approximation theorem, we will research the capability of
neural networks to learn the underlying physical laws
driving computational models and consequently whether they
can circumvent the need for accurate numerical solvers like
the finite element method, which is known to be
computationally complex. In addition, we will explore other
applications such as the discovery of governing laws that
cannot be captured in a closed form and the full
differentiability of neural-network-based physics solvers.
Finally, the results of these investigations will be
integrated into an existing prototype and validated
clinically. | Projektleitung: Tommaso Mansi
Beteiligte: Tiziano Passerini, Viorel Mihalef
Stichwörter: Medical Image Processing; Computational Modeling; Machine Learning
Laufzeit: 1.10.2018 - 30.9.2021
Förderer: Siemens Healthineers GmbH
Kontakt: Meister, Felix Telefon +49 9131 85 28977, Fax +49 9131 85 27270, E-Mail: felix.meister@fau.de
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