132273 20260217205506.0 doi 10.1016/j.medengphy.2023.104092 sideral 137454 ART-2024-137454 eng Sainz de Mena, D. Universidad de Zaragoza (orcid)0000-0001-7452-0437 Exploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion 2024 Access copy available to the general public Unrestricted Dynamic 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. info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00 info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS 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/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21 info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.3 2024 ENGINEERING, BIOMEDICAL 81 / 124 = 0.653 2024 Q3 T2 0.525 2024 Biophysics 2024 Q3 Biomedical Engineering 2024 Q3 4.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Pérez, M. A. Universidad de Zaragoza (orcid)0000-0002-2901-4188 García-Aznar, J. M. Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 123 (2024), 104092 [10 pp.] Med. eng. phys. MEDICAL ENGINEERING & PHYSICS 1350-4533 1966877 http://zaguan.unizar.es/record/132273/files/texto_completo.pdf Versión publicada 2532231 http://zaguan.unizar.es/record/132273/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:132273 articulos driver 2026-02-17-20:23:39 ARTICLE