2026-04-28T18:04:26Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/83712024-06-06T22:42:07Zcom_10259_6229com_10259_4534com_10259.4_106com_10259_2604col_10259_6230

Carpio, Ana b539a1aa-926d-49cc-8122-e1d0588259dd 600 Cebrián de Barrio, Elena 755 600 0000-0001-8389-0907 Gutiérrez, Andrea 8869d2aa-3fa4-4e6f-b464-f7610eee0dd9 600 2024-01-17T11:55:52Z 2024-01-17T11:55:52Z 2023 0266-5611 http://hdl.handle.net/10259/8371 10.1088/1361-6420/acd5f8 1361-6420 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. 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. application/pdf eng IOP Publishing Inverse Problems. 2023, V. 39, n. 7, 075007 https://doi.org/10.1088/1361-6420/acd5f8 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Inverse scattering Full waveform inversion Topological energy Bayesian inference Markov Chain Monte Carlo PDE constrained optimization Laplace approximation Matemáticas Ciencias biomédicas Mathematics Medical sciences Object based Bayesian full-waveform inversion for shear elastography info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Inverse Problems 39 7 075007 THUMBNAIL Carpio-ip_2023.pdf.jpg Carpio-ip_2023.pdf.jpg IM Thumbnail image/jpeg 3660 https://riubu.ubu.es/bitstream/10259/8371/4/Carpio-ip_2023.pdf.jpg ead17607f6746b19fa745d7d8a30918c MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/8371/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/8371/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Carpio-ip_2023.pdf Carpio-ip_2023.pdf application/pdf 1032074 https://riubu.ubu.es/bitstream/10259/8371/1/Carpio-ip_2023.pdf f85d9a6c97bc686392f42a9df69e9152 MD5 1 10259/8371 oai:riubu.ubu.es:10259/8371 2024-06-07 00:42:07.609 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIA0KQVVUT1JJWkEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSBkaWZ1bmRpciwgZGUgbWFuZXJhIGdyYXR1aXRhLCBlbCBjb250ZW5pZG8gZGUgbG9zIGFyY2hpdm9zIGRpZ2l0YWxlcyBxdWUgY29ycmVzcG9uZGVuIGFsIGRvY3VtZW50byBkZXNjcml0byBhbnRlcmlvcm1lbnRlLCBjb24gY2Fyw6FjdGVyIG5vIGV4Y2x1c2l2byB5IGRlIG1hbmVyYSBww7pibGljYSBlbiBhY2Nlc28gYWJpZXJ0byBhIHRyYXbDqXMgZGUgSW50ZXJuZXQsIHBhcmEgbG8gcXVlIGxhIEJpYmxpb3RlY2EgcHJvY2VkZXLDoSBhIGFyY2hpdmFybG9zIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwuIEFzaW1pc21vIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgZGUgQnVyZ29zIGEgcmVhbGl6YXIgbGFzIHRyYW5zZm9ybWFjaW9uZXMgbmVjZXNhcmlhcyBkZSBmb3JtYXRvLCBubyBkZSBjb250ZW5pZG8sIHBhcmEgZ2FyYW50aXphciBsYSBwcmVzZXJ2YWNpw7NuIHkgZWwgYWNjZXNvIGVuIGVsIGZ1dHVyby4NCg0KRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLg0KDQpMYSBjZXNpw7NuIGRlIGRlcmVjaG9zIGRlIGVzdGEgb2JyYSBzZSBlbmN1ZW50cmEgc3VqZXRhIGEgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3Iu