2026-06-27T11:24:43Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/51112024-07-09T14:24:38Zcom_20.500.14352_14col_20.500.14352_20
Docta Complutense author Carpio Rodríguez, Ana María author Cebrián, Elena 2023-06-16T14:48:27Z 2023-06-16T14:48:27Z 2021-12 https://hdl.handle.net/20.500.14352/5111 Elastography is a noninvasive medical imaging technique that aims to visualize the elastic properties of soft tissue. The underlying idea is that elastic behavior can distinguish healthy from unhealthy tissue in specific situations. For instance, cancerous tumors and scarred liver tissue will often be stiffer than the surrounding healthy tissue. Elastography based devices emit elastic waves that interact with the tissue under study and record the response received. Using the recorded data, adequate mathematical algorithms provide information on stiffness variations in the explored region. Such devices are already being used for the diagnosis of liver and prostate diseases. However, there is a need for improved methods and algorithms, allowing to resolve for tiny tumors or for little contrast regions with quantified uncertainty. Here, we propose a full wave form inversion scheme for localized inhomogeneities that proceeds as follows. First, we identify the most prominent anomalous regions in the tissue by visualizing topological fields associated to functionals comparing the true recorded data with the data that would be obtained varying the stiffness fields. Then, we improve this information by optimization strategies. Finally, we quantify uncertainty in the outcome resorting to a Bayesian inversion framework. We illustrate the method using data from prostate and liver studies. eng Full-waveform inversion algorithms with quantified uncertainty for medical shear elastography conference paper URL https://docta.ucm.es/bitstreams/e21a3556-9039-4e7a-95b7-6dddb8ddd2f6/download File MD5 dd89351a644ebec5bd1d8bec3f28114a 62601 application/pdf abstract mist21.pdf URL https://docta.ucm.es/bitstreams/5e35547f-b0db-470e-ace9-c203543a3d24/download File MD5 3781b5fa79e3f2d53ad4ba4543230f78 2602 text/plain abstract mist21.pdf.txt