Evaluating the performance of poroelastic and poroviscoelastic models in intrinsic MR elastography

Motivation: Previous work showed that viscoelastic property reconstruction results in data-model mismatch and non-unique solutions in Intrinsic Magnetic Resonance Elastography (iMRE) and suggested that a poroelastic model may address these challenges. Goal(s): To evaluate and compare poroelastic and poroviscoelastic brain models in iMRE for the brain, and to assess the influence of two different driving frequencies. Approach: Poro- and poroviscous properties were reconstructed using the same displacement data set, and the performance was evaluated through spatial consistency, repeatability and left-right symmetry. Results: Both models performed well, with the poroviscoelastic model being the most consistent. The second harmonic proved valuable for multifrequency reconstruction. Impact: These results directly contribute to the improvement of iMRE by addressing previous limitations and by demonstrating the feasibility of poro(visco)elastic reconstruction, potentially enabling novel ways of studying cerebral microvasculature and (mechanical) neurovascular coupling in the brain.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4754
Primary Topic
Elasticity and Material Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating the performance of poroelastic and poroviscoelastic models in intrinsic MR elastography

Marius Burman Ingeberg, Jaco J.M. Zwanenburg, Eli van Houten
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Elasticity and Material Modeling
article

Evaluating the performance of poroelastic and poroviscoelastic models in intrinsic MR elastography

Marius Burman Ingeberg, Jaco J.M. Zwanenburg, Eli van Houten
article en

Abstract

Motivation: Previous work showed that viscoelastic property reconstruction results in data-model mismatch and non-unique solutions in Intrinsic Magnetic Resonance Elastography (iMRE) and suggested that a poroelastic model may address these challenges. Goal(s): To evaluate and compare poroelastic and poroviscoelastic brain models in iMRE for the brain, and to assess the influence of two different driving frequencies. Approach: Poro- and poroviscous properties were reconstructed using the same displacement data set, and the performance was evaluated through spatial consistency, repeatability and left-right symmetry. Results: Both models performed well, with the poroviscoelastic model being the most consistent. The second harmonic proved valuable for multifrequency reconstruction. Impact: These results directly contribute to the improvement of iMRE by addressing previous limitations and by demonstrating the feasibility of poro(visco)elastic reconstruction, potentially enabling novel ways of studying cerebral microvasculature and (mechanical) neurovascular coupling in the brain.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Elasticity and Material Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.