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Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function
Andreas Solheim1, Geir Ringstad2, Per Kristian Eide3
1Department of Numerical Analysis and Scientific Computing (SCAN), Simula Research Laboratories, Kristian Augusts gate 23, Oslo, 0164, Norway; Department of Mathematics, University of Oslo, Moltke Moes vei 35, Oslo, 0851, Norway.
This study introduces a novel computational method using model order reduction to accelerate brain simulations for patient-specific analysis. This approach significantly reduces computational costs for modeling brain waste clearance and glymphatic function.
Area of Science:
- Computational neuroscience
- Biomedical engineering
- Medical imaging analysis
Background:
- Computational brain modeling is crucial for understanding metabolic waste clearance but faces scalability challenges.
- Patient-specific brain models are currently computationally prohibitive for large-scale simulations.
- Existing methods struggle to meet the demands of high-throughput, personalized brain modeling.
Purpose of the Study:
- To develop and validate a novel computational approach for accelerating patient-specific brain models.
- To leverage model order reduction (MOR) techniques to decrease computational costs in brain simulations.
- To enable more feasible and scalable computational modeling of brain functions like glymphatic transport.
Main Methods:
- Utilized image registration based on magnetic resonance imaging (MRI) to create inter-brain mappings.
- Applied model order reduction techniques to computational models of brain geometries.
- Mapped previously computed simulation solutions onto new patient-specific brain geometries.
- Investigated the approach on two glymphatic function modeling problems using a dataset of 101 human MRIs.
Main Results:
- Achieved a speedup factor exceeding 750 times compared to full-order simulations for both example problems.
- Introduced minimal additional system assembly overhead.
- Maintained accuracy, with reduced solutions recovering full-order solutions within a 10% error margin in most cases.
- Demonstrated applicability across different neurological conditions, including idiopathic Normal Pressure Hydrocephalus.
Conclusions:
- Model order reduction, integrated with inter-brain mapping from MRI data, significantly accelerates patient-specific brain simulations.
- This novel technique makes high-throughput computational modeling of brain functions, such as glymphatic transport, more feasible.
- The method holds promise for advancing personalized medicine in neuroscience by enabling efficient patient-specific computational analysis.
