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.

翻译：心室内矢量流映射（iVFM）旨在增强和量化心脏成像中的彩色多普勒。在本研究中，我们通过利用物理信息神经网络（PINNs）和一种基于物理引导的nnU-Net监督方法，为传统的iVFM优化方案提出了新颖的替代方案。在基于患者特异性计算流体动力学模型模拟的彩色多普勒图像以及活体多普勒采集数据上进行评估时，两种方法均展现出与原始iVFM算法相当的重建性能。PINNs的效率通过双阶段优化和预优化权重得到提升。另一方面，nnU-Net方法在泛化能力和实时性方面表现优异。值得注意的是，nnU-Net在稀疏和截断的多普勒数据上展现出卓越的鲁棒性，同时保持了对显式边界条件的独立性。总体而言，我们的结果凸显了这些方法在重建心室内矢量血流方面的有效性。该研究还提示了PINNs在超快彩色多普勒成像中的潜在应用，以及通过结合流体动力学方程来推导基于血流的心血管疾病生物标志物的可能性。