Exploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion

Sainz de Mena, D. (Universidad de Zaragoza) ; Pérez, M. A. (Universidad de Zaragoza) ; García-Aznar, J. M. (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.medengphy.2023.104092

Año: 2024

Publicado en: MEDICAL ENGINEERING & PHYSICS 123 (2024), 104092 [10 pp.]
ISSN: 1350-4533

Factor impacto JCR: 2.3 (2024)

Categ. JCR: ENGINEERING, BIOMEDICAL rank: 81 / 124 = 0.653 (2024) - Q3 - T2

Factor impacto SCIMAGO: 0.525 - Biophysics (Q3) - Biomedical Engineering (Q3)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113819RB-I00
Financiación: info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS
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
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033

Tipo y forma: Article (Published version)

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