RT info:eu-repo/semantics/article
T1 Object based Bayesian full-waveform inversion for shear elastography
A1 Carpio, Ana
A1 Cebrián de Barrio, Elena
A1 Gutiérrez, Andrea
K1 Inverse scattering
K1 Full waveform inversion
K1 Topological energy
K1 Bayesian inference
K1 Markov Chain Monte Carlo
K1 PDE constrained optimization
K1 Laplace approximation
K1 Matemáticas
K1 Mathematics
K1 Biomedicina
K1 Biomedicine
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
YR 2023
FD 2023
LK http://hdl.handle.net/10259/8371
UL http://hdl.handle.net/10259/8371
LA eng
NO This research has been partially supported by the FEDER /Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación grants No. MTM2017-84446-C2-1-R and PID2020- 112796RB-C21.
DS Repositorio Institucional de la Universidad de Burgos
RD 08-may-2024