Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
This work addresses intraventricular vector flow mapping for cardiac imaging, offering incremental improvements in efficiency and robustness.
The study tackled the problem of reconstructing intraventricular vector blood flow from color Doppler images by proposing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach, achieving comparable performance to the original iVFM algorithm with nnU-Net showing superior robustness on sparse and truncated data.
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. When evaluated 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.