Object based Bayesian full-waveform inversion for shear elastography. (arXiv:2305.06646v1 [math.NA])

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 …

arxiv bayesian bayesian inference computational data fields framework geometry imaging inference math posterior probability uncertainty