RT Journal Article
T1 Object based Bayesian full-waveform inversion for shear elastography
A1 Carpio Rodríguez, Ana María
A1 Cebrián, Elena
A1 Gutiérrez, Andrea
AB We develop a computational framework to quantify uncertainty in shear elastography imaging of anomalies in tissues. We adopt a Bayesian inference formulation. Given the observed data, a forward model and their uncertainties, we find the posterior probability of parameter fields representing the geometry of the anomalies and their shear moduli. To construct a prior probability, we exploit the topological energies of associated objective functions. We demonstrate the approach on synthetic two dimensional tests with smooth and irregular shapes. Sampling the posterior distribution by Markov Chain Monte Carlo (MCMC) techniques we obtain statistical information on the shear moduli and the geometrical properties of the anomalies. General affine-invariant ensemble MCMC samplers are adequate for shapes characterized by parameter sets of low to moderate dimension. However, MCMC methods are computationally expensive. For simple shapes, we devise a fast optimization scheme to calculate the maximum a posteriori (MAP) estimate representing the most likely parameter values. Then, we approximate the posterior distribution by a Gaussian distribution found by linearization about the MAP point to capture the main mode at a low computational cost.
PB IOP Publishing
SN 0266-5611
SN 1361-6420
YR 2023
FD 2023-06-06
LK https://hdl.handle.net/20.500.14352/93910
UL https://hdl.handle.net/20.500.14352/93910
LA eng
NO Carpio Rodríguez, A. M., Cebrián, E. y Gutiérrez, A. «Object based Bayesian full-waveform inversion for shear elastography». Inverse Problems, vol. 39, n.o 7, julio de 2023, p. 075007. DOI.org (Crossref), https://doi.org/10.1088/1361-6420/acd5f8.
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 26 abr 2025