Ultrasound deep learning for monitoring of flow–vessel dynamics in murine carotid artery

Abstract

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.