Several pathological phenomena are closely associated with mechanical properties of vessel and interactions of blood flow–wall dynamics. However, conventional techniques cannot easily measure these features. In this study, new deep learning-based simultaneous measurement of flow–wall dynamics (DL-SFW) is proposed by devising integrated neural network for super-resolved localization and vessel wall segmentation and combining with tissue motion measurement technique and flow velocimetry.
An accurate analysis of fluid–structure interaction (FSI) at compliant arteries via ultrasound (US) imaging and numerical modeling is a limitation of several studies. In this study, we propose a deep learning-based boundary detection and compensation (DL-BDC) technique that can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in near-wall flow dynamics. The segmentation performance of the technique is evaluated through numerical simulations with synthetic US images and in vitro experiments.
In this study, adaptive hybrid (AH) scheme is proposed to enhance the measurement accuracy of conventional CD method to acquire the 2-dimensional velocity field of blood flows. It can offer the assistance of the velocity field information measured preliminarily using ultrasound speckle image velocimetry (SIV) technique. Consequently, erroneous vectors in the CD results were replaced with the SIV results. The performance of the proposed AH method was validated by varying flow rate and insonation angle. We compared the AH method with the CD and SIV methods in an agarose vessel model.