Verma Y, Schattenfroh J, Sack I, Budday S, Steinmann P, Heltai L (2026)
Publication Language: English
Publication Type: Other publication type
Publication year: 2026
DOI: 10.48550/arXiv.2604.02435
Magnetic Resonance Elastography (MRE) has become an essential tool in
assessing the mechanical properties of soft tissues in-vivo, prompting
significant progress in new inversion algorithms. This creates a need
for a benchmarking framework to promote uniformity and accessibility. To
address this, we introduce a comprehensive in-silico dataset acquired
by solving the forward Finite Element calculations of shear wave
propagation in a linear visco-elastic material. This dataset aims to
serve as a platform for evaluating inversion schemes by providing data
that can be used as input with known mechanical properties to these
methods. It includes simulations on homogeneous cuboidal domains of
varying spatial and temporal resolution, and an extension to more
physiological variations, including material inhomogeneity and internal
arterial pulsation. We present a comprehensive case study using
simulated data as an input to a direct inversion (DI) scheme, which
allows for an expedient local inversion into the underlying material
parameters. When aiming to reconstruct the parameters describing the
linear visco-elastic material behavior via DI, we find that due to
compromised convergence properties of frequency-domain stencils,
stemming from truncation and subtractive cancellation errors, the
reconstruction accuracy depends non-monotonically on the spatial and
temporal resolution of the measurement grid. For inhomogeneous domains,
the reconstruction was successful with notable interface boundaries. In
the presence of pressurized vascular inclusions, a general stiffening of
the domain was noted, as the recovered shear modulus was higher than
the one assumed in forward modeling. Our study highlights the potential
of this dataset as a vital benchmarking tool for advancing the
development and refinement of MRE techniques, contributing to more
accurate and reliable assessment of soft tissue mechanics.
APA:
Verma, Y., Schattenfroh, J., Sack, I., Budday, S., Steinmann, P., & Heltai, L. (2026). Simulation Platform To Evaluate Inversion Techniques For Magnetic Resonance Elastography Data.
MLA:
Verma, Yashasvi, et al. Simulation Platform To Evaluate Inversion Techniques For Magnetic Resonance Elastography Data. 2026.
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