Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
Hang Jung Ling Salomé Bru Julia Puig Florian Vixège Simon Mendez Franck Nicoud Pierre-Yves Courand Olivier Bernard Damien Garcia
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify
color Doppler in cardiac imaging. In this study, we propose novel alternatives
to the traditional iVFM optimization scheme by utilizing physics-informed
neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach.
Through rigorous evaluation on simulated color Doppler images derived from a
patient-specific computational fluid dynamics model and in vivo Doppler
acquisitions, both approaches demonstrate comparable reconstruction performance
to the original iVFM algorithm. The efficiency of PINNs is boosted through
dual-stage optimization and pre-optimized weights. On the other hand, the
nnU-Net method excels in generalizability and real time capabilities. Notably,
nnU-Net shows superior robustness on sparse and truncated Doppler data while
maintaining independence from explicit boundary conditions. Overall, our
results highlight the effectiveness of these methods in reconstructing
intraventricular vector blood flow. The study also suggests potential
applications of PINNs in ultrafast color Doppler imaging and the incorporation
of fluid dynamics equations to derive biomarkers for cardiovascular diseases
based on blood flow.