Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.
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Abstract
Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial wall stiffness. Such techniques rely upon motion detection algorithms that provide arterial wall displacement estimation. 
Approach: In this study, we propose an unsupervised deep learning-based approach, originally proposed for image registration, in order to enable improved quality arterial wall displacement estimation at high temporal and spatial resolutions. The performance of the proposed network was assessed through phantom experiments, where various models were trained by using ultrasound RF signals, or B-mode images, as well as different loss functions. 
Main results: Using the mean square error (MSE) for the training process provided the highest signal-to-noise ratio when training on the B-modes images (30.36±1.14 dB) and highest contrast-to-noise ratio when training on the RF signals (32.84±1.89 dB). In addition, training the model on RF signals demonstrated the capability of providing accurate localized pulse wave velocity (PWV) maps, with a mean relative error (MREPWV) of 3.32±1.80 % and an R2 of 0.97±0.03. Finally, the developed model was tested in human common carotid arteries in-vivo, providing accurate tracking of the distension pulse wave propagation, with an MREPWV= 3.86 ±2.69 % and R2=0.95 ± 0.03. 
Significance: In conclusion, a novel displacement estimation approach was presented, showing promise in improving vascular elasticity imaging techniques.
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