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Updated: Feb 19, 2026

Magnetic Resonance Elastography Methodology for the Evaluation of Tissue Engineered Construct Growth
Published on: February 9, 2012
Magnetic Resonance Elastography With Incremental Compressive Prestrain as a Method for Mapping Ex Vivo Brain Tissue
Olivia Bailey1,2, Alexa M Diano1,2, Ali H Lateef1,2
1Department of Biomedical Engineering, University of Delaware, Newark, DE 19713.
None:
Traumatic brain injury (TBI), resulting from blunt or blast impact to the head, affects approximately 1.6 million Americans per year. Brain biomechanics and injury models play a fundamental role in the TBI community as they can be used to evaluate protective equipment, refine safety standards, and improve individual treatment plans. To maximize clinical relevance, these models require accurate and precise mechanical input parameters relevant to expected injuries. Magnetic resonance elastography (MRE) offers a noninvasive method to quantify the viscoelastic properties of soft tissues by phase-encoding mechanical waves as they propagate through a tissue of interest. While MRE has advanced our understanding of brain tissue mechanics, characterizing nonlinear behavior under large deformations remains a critical challenge for improving TBI models. Here, we coupled ex vivo MRE with incremental compression to characterize brain tissue mechanics beyond the linear elastic and linear viscoelastic regimes, which are more relevant for injury. Using a custom-made MR-compatible compression device, controlled axial prestrain was applied to fresh brain tissue-agar phantoms. On average, the tissue storage modulus increased by 73.6% when approximately 6.4% of strain was applied. The resulting experimental data, namely, storage modulus and applied prestrain, were successfully fit to a phenomenological equation to output material parameters, including a nonlinearity metric. These findings provide novel, strain-dependent mechanical data for brain tissue that extend beyond small-strain assumptions. The results will improve computational models of TBI by supplying mechanical input parameters for predicting brain response under injury-relevant loading conditions.
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