Several arterial diseases are closely related with mechanical properties of the blood vessel and interactions of flow–vessel dynamics such as mean flow velocity, wall shear stress (WSS) and vascular strain. However, there is an opportunity to improve the measurement accuracy of vascular properties and hemodynamics by adopting deep learning-based ultrasound imaging for flow–vessel dynamics (DL-UFV). In this study, the DL-UFV is proposed by devising an integrated neural network for super-resolved localization and vessel wall segmentation, and it is also combined with tissue motion estimation and flow measurement techniques such as speckle image velocimetry and speckle tracking velocimetry for measuring velocity field information of blood flow. Performance of the DL-UFV is verified by comparing with other conventional techniques in tissue-mimicking phantoms. After the performance verification, in vivo feasibility is demonstrated in the murine carotid artery with different pathologies: aging and diabetes mellitus (DM). The mutual comparison of flow–vessel dynamics and histological analyses shows correlations between the immunoreactive region and abnormal flow–vessel dynamics interactions. The DL-UFV improves biases in measurements of velocity, WSS, and strain with up to 4.6-fold, 15.1-fold, and 22.2-fold in the tissue-mimicking phantom, respectively. Mean flow velocities and WSS values of the DM group decrease by 30% and 20% of those of the control group, respectively. Mean flow velocities and WSS values of the aging group (34.11 cm/s and 13.17 dyne/cm²) are slightly smaller than those of the control group (36.22 cm/s and 14.25 dyne/cm²). However, the strain values of the aging and DM groups are much smaller than those of the control group (p < 0.05). This study shows that the DL-UFV performs better than the conventional ultrasound-based flow and strain measurement techniques for measuring vascular stiffness and complicated flow–vessel dynamics. Furthermore, the DL-UFV demonstrates its excellent performance in the analysis of the hemodynamic and hemorheological effects of DM and aging on the flow and vascular characteristics. This work provides useful hemodynamic information, including mean flow velocity, WSS and strain with high-resolution for diagnosing the pathogenesis of arterial diseases. This information can be used for monitoring progression and regression of atherosclerotic diseases in clinical practice.

几种动脉疾病与血管的机械特性和血流-血管动力学的相互作用密切相关，例如平均流速、壁剪切应力 (WSS) 和血管应变。然而，通过采用基于深度学习的血流血管动力学超声成像 (DL-UFV) 可以提高血管特性和血流动力学的测量精度。在这项研究中，DL-UFV 是通过设计一个集成的神经网络来实现超分辨率定位和血管壁分割，并结合组织运动估计和流量测量技术，如散斑图像测速和散斑跟踪测速进行测量。血流速度场信息。DL-UFV 的性能通过与组织模拟模型中的其他传统技术进行比较来验证。在性能验证之后，在具有不同病理的小鼠颈动脉中证明了体内可行性：衰老和糖尿病 (DM)。流动-血管动力学和组织学分析的相互比较显示了免疫反应区域和异常流动-血管动力学相互作用之间的相关性。DL-UFV 在组织模拟模型中将速度、WSS 和应变测量的偏差分别提高了 4.6 倍、15.1 倍和 22.2 倍。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 在性能验证之后，在具有不同病理的小鼠颈动脉中证明了体内可行性：衰老和糖尿病 (DM)。流动-血管动力学和组织学分析的相互比较显示了免疫反应区域和异常流动-血管动力学相互作用之间的相关性。DL-UFV 在组织模拟模型中将速度、WSS 和应变测量的偏差分别提高了 4.6 倍、15.1 倍和 22.2 倍。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 在性能验证之后，在具有不同病理的小鼠颈动脉中证明了体内可行性：衰老和糖尿病 (DM)。流动-血管动力学和组织学分析的相互比较显示了免疫反应区域和异常流动-血管动力学相互作用之间的相关性。DL-UFV 在组织模拟模型中将速度、WSS 和应变测量的偏差分别提高了 4.6 倍、15.1 倍和 22.2 倍。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 流动-血管动力学和组织学分析的相互比较显示了免疫反应区域和异常流动-血管动力学相互作用之间的相关性。DL-UFV 在组织模拟模型中将速度、WSS 和应变测量的偏差分别提高了 4.6 倍、15.1 倍和 22.2 倍。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 流动-血管动力学和组织学分析的相互比较显示了免疫反应区域和异常流动-血管动力学相互作用之间的相关性。DL-UFV 在组织模拟模型中将速度、WSS 和应变测量的偏差分别提高了 4.6 倍、15.1 倍和 22.2 倍。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 分别。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm 分别。DM组的平均流速和WSS值分别比对照组降低30%和20%。老化组的平均流速和 WSS 值（34.11 cm/s 和 13.17 dyne/cm² ) 略小于对照组 (36.22 cm/s 和 14.25 dyne/cm ² )。然而，老化组和 DM 组的应变值远小于对照组（p < 0.05)。本研究表明，DL-UFV 在测量血管刚度和复杂的血流-血管动力学方面优于传统的基于超声的流量和应变测量技术。此外，DL-UFV 在分析 DM 和老化对流动和血管特征的血流动力学和血液流变学影响方面表现出优异的性能。这项工作提供了有用的血流动力学信息，包括平均流速、WSS 和高分辨率的应变，用于诊断动脉疾病的发病机制。该信息可用于监测临床实践中动脉粥样硬化疾病的进展和消退。