000120987 001__ 120987
000120987 005__ 20240319081019.0
000120987 0247_ $$2doi$$a10.1007/s00366-022-01667-w
000120987 0248_ $$2sideral$$a129952
000120987 037__ $$aART-2022-129952
000120987 041__ $$aeng
000120987 100__ $$0(orcid)0000-0001-7452-0437$$aSainz-DeMena, Diego$$uUniversidad de Zaragoza
000120987 245__ $$aA finite element based optimization algorithm to include diffusion into the analysis of DCE-MRI
000120987 260__ $$c2022
000120987 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120987 5203_ $$aPharmacokinetic (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.
000120987 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00$$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541
000120987 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120987 590__ $$a8.7$$b2022
000120987 592__ $$a1.096$$b2022
000120987 591__ $$aENGINEERING, MECHANICAL$$b4 / 136 = 0.029$$c2022$$dQ1$$eT1
000120987 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b12 / 110 = 0.109$$c2022$$dQ1$$eT1
000120987 593__ $$aComputer Science Applications$$c2022$$dQ1
000120987 593__ $$aSoftware$$c2022$$dQ1
000120987 593__ $$aModeling and Simulation$$c2022$$dQ1
000120987 593__ $$aEngineering (miscellaneous)$$c2022$$dQ1
000120987 594__ $$a13.4$$b2022
000120987 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120987 700__ $$aYe, Wenfeng
000120987 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, María Ángeles$$uUniversidad de Zaragoza
000120987 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000120987 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000120987 773__ $$g38 (2022), 3849–3865$$pEng. comput.$$tENGINEERING WITH COMPUTERS$$x0177-0667
000120987 8564_ $$s5148394$$uhttps://zaguan.unizar.es/record/120987/files/texto_completo.pdf$$yVersión publicada
000120987 8564_ $$s2434274$$uhttps://zaguan.unizar.es/record/120987/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120987 909CO $$ooai:zaguan.unizar.es:120987$$particulos$$pdriver
000120987 951__ $$a2024-03-18-16:00:46
000120987 980__ $$aARTICLE