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.

通过借助物理导向的神经网络（PINNs）和基于物理引导的 nnU-Net 监督方法，以及对模拟和实际彩色多普勒图像进行的严格评估，本研究提出了传统 iVFM 优化方案的新替代方法，显示出与原始 iVFM 算法相当的重建性能。同时，研究指出 nnU-Net 方法在稀疏和截断多普勒数据方面表现出卓越的鲁棒性，同时保持与明确边界条件的独立性，进而突显了这些方法在重建室间心室血流方面的有效性，并暗示了基于血流的流体动力学方程导出心血管疾病生物标志物的潜在应用。