A finite element based optimization algorithm to include diffusion into the analysis of DCE-MRI

Sainz-DeMena, Diego (Universidad de Zaragoza) ; Ye, Wenfeng ; Pérez, María Ángeles (Universidad de Zaragoza) ; García-Aznar, José Manuel (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1007/s00366-022-01667-w

Año: 2022

Publicado en: ENGINEERING WITH COMPUTERS 38 (2022), 3849–3865
ISSN: 0177-0667

Factor impacto JCR: 8.7 (2022)

Categ. JCR: ENGINEERING, MECHANICAL rank: 4 / 136 = 0.029 (2022) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 12 / 110 = 0.109 (2022) - Q1 - T1

Factor impacto CITESCORE: 13.4 - Engineering (Q1) - Mathematics (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 1.096 - Computer Science Applications (Q1) - Software (Q1) - Modeling and Simulation (Q1) - Engineering (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00
Financiación: 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
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)