Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function

TL;DR

This paper presents a novel model order reduction approach for brain geometry simulations, enabling efficient patient-specific modeling of glymphatic function using MRI data and image registration techniques.

Contribution

It introduces a new method leveraging model order reduction and image registration to efficiently adapt precomputed solutions to individual brain geometries.

Findings

Method successfully applied to 101 patient MRI datasets.

Effective in modeling glymphatic function in both healthy and diseased brains.

Reduces computational costs for patient-specific brain simulations.

Abstract

Computational modeling of the brain has become a key part of understanding how the brain clears metabolic waste, but patient-specific modeling on a significant scale is still out of reach with current methods. We introduce a novel approach for leveraging model order reduction techniques in computational models of brain geometries to alleviate computational costs involved in numerical simulations. Using image registration methods based on magnetic resonance imaging, we compute inter-brain mappings which allow previously computed solutions on other geometries to be mapped on to a new geometry. We investigate this approach on two example problems typical of modeling of glymphatic function, applied to a dataset of 101 MRI of human patients. We discuss the applicability of the method when applied to a patient with no known neurological disease, as well as a patient diagnosed with idiopathic Normal Pressure Hydrocephalus displaying significantly enlarged ventricles