Speckle image velocimetry
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.
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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.
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