120987 20240319081019.0 doi 10.1007/s00366-022-01667-w sideral 129952 ART-2022-129952 eng Sainz-DeMena, Diego Universidad de Zaragoza (orcid)0000-0001-7452-0437 A finite element based optimization algorithm to include diffusion into the analysis of DCE-MRI 2022 Access copy available to the general public Unrestricted Pharmacokinetic (PK) models are used to extract physiological information from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) sequences. Some of the most common models employed in clinical practice, such as the standard Tofts model (STM) or the extended Tofts model (ETM), do not account for passive delivery of contrast agent (CA) through diffusion. In this work, we introduce a diffusive term based on the concept of effective diffusivity into a finite element (FE) implementation of the ETM formulation, obtaining a new formulation for the diffusion-corrected ETM (D-ETM). A gradient-based optimization algorithm is developed to characterize the vascular properties of the tumour from the CA concentration curves obtained from imaging clinical data. To test the potential of our approach, several theoretical distributions of CA concentration are generated on a benchmark problem and a real tumour geometry. The vascular properties used to generate these distributions are estimated from an inverse analysis based on both the ETM and the D-ETM approaches. The outcome of these analyses shows the limitations of the ETM to retrieve accurate parameters in the presence of diffusion. The ETM returns smoothed distributions of vascular properties, reaching unphysical values in some of them, while the D-ETM accurately depicted the heterogeneity of K-Trans, nu(e) and nu(p) distributions (mean absolute relative difference (ARD) of 16%, 15% and 9%, respectively, for the real geometry case) keeping all their values within their physiological ranges, outperforming the ETM. info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00 info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 8.7 2022 ENGINEERING, MECHANICAL 4 / 136 = 0.029 2022 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 12 / 110 = 0.109 2022 Q1 T1 1.096 2022 Computer Science Applications 2022 Q1 Software 2022 Q1 Modeling and Simulation 2022 Q1 Engineering (miscellaneous) 2022 Q1 13.4 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ye, Wenfeng Pérez, María Ángeles Universidad de Zaragoza (orcid)0000-0002-2901-4188 García-Aznar, José Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 38 (2022), 3849–3865 Eng. comput. ENGINEERING WITH COMPUTERS 0177-0667 5148394 http://zaguan.unizar.es/record/120987/files/texto_completo.pdf Versión publicada 2434274 http://zaguan.unizar.es/record/120987/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:120987 articulos driver 2024-03-18-16:00:46 ARTICLE