000132273 001__ 132273
000132273 005__ 20250923084412.0
000132273 0247_ $$2doi$$a10.1016/j.medengphy.2023.104092
000132273 0248_ $$2sideral$$a137454
000132273 037__ $$aART-2024-137454
000132273 041__ $$aeng
000132273 100__ $$0(orcid)0000-0001-7452-0437$$aSainz de Mena, D.$$uUniversidad de Zaragoza
000132273 245__ $$aExploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion
000132273 260__ $$c2024
000132273 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132273 5203_ $$aDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used to assess tissue vascularization, particularly in oncological applications. However, the most widely used pharmacokinetic (PK) models do not account for contrast agent (CA) diffusion between neighboring voxels, which can limit the accuracy of the results, especially in cases of heterogeneous tumors. To address this issue, previous works have proposed algorithms that incorporate diffusion phenomena into the formulation. However, these algorithms often face convergence problems due to the ill-posed nature of the problem. In this work, we present a new approach to fitting DCE-MRI data that incorporates CA diffusion by using Physics-Informed Neural Networks (PINNs). PINNs can be trained to fit measured data obtained from DCE-MRI while ensuring the mass conservation equation from the PK model. We compare the performance of PINNs to previous algorithms on different 1D cases inspired by previous works from literature. Results show that PINNs retrieve vascularization parameters more accurately from diffusion-corrected tracer-kinetic models. Furthermore, we demonstrate the robustness of PINNs compared to other traditional algorithms when faced with noisy or incomplete data. Overall, our results suggest that PINNs can be a valuable tool for improving the accuracy of DCE-MRI data analysis, particularly in cases where CA diffusion plays a significant role.
000132273 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00$$9info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS$$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$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033
000132273 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000132273 590__ $$a2.3$$b2024
000132273 592__ $$a0.525$$b2024
000132273 591__ $$aENGINEERING, BIOMEDICAL$$b81 / 124 = 0.653$$c2024$$dQ3$$eT2
000132273 593__ $$aBiophysics$$c2024$$dQ3
000132273 593__ $$aBiomedical Engineering$$c2024$$dQ3
000132273 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000132273 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, M. A.$$uUniversidad de Zaragoza
000132273 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, J. M.$$uUniversidad de Zaragoza
000132273 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000132273 773__ $$g123 (2024), 104092 [10 pp.]$$pMed. eng. phys.$$tMEDICAL ENGINEERING & PHYSICS$$x1350-4533
000132273 8564_ $$s1966877$$uhttps://zaguan.unizar.es/record/132273/files/texto_completo.pdf$$yVersión publicada
000132273 8564_ $$s2532231$$uhttps://zaguan.unizar.es/record/132273/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000132273 909CO $$ooai:zaguan.unizar.es:132273$$particulos$$pdriver
000132273 951__ $$a2025-09-22-14:30:51
000132273 980__ $$aARTICLE