Key result
An unsupervised deep learning-based displacement estimation model provided accurate tracking of distension pulse wave propagation in human carotid arteries in vivo (mean relative error 3.86 ± 2.69%).
Why the study?
Ultrasound vascular elasticity imaging relies on motion detection algorithms to estimate arterial wall displacement, requiring improved quality estimation at high temporal and spatial resolutions.
An unsupervised deep learning approach improves arterial wall displacement estimation for vascular elasticity imaging, demonstrating high accuracy in both phantom and in vivo human carotid artery models.
Objective . 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 (MRE PWV ) of 3.32 ± 1.80% and an R 2 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 MRE PWV = 3.86 ± 2.69% and R 2 = 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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Karageorgos et al. (2023) studied Arterial wall stiffness. Unsupervised deep learning-based displacement estimation was evaluated on Pulse wave velocity (PWV) map accuracy (mean relative error and R2). An unsupervised deep learning-based displacement estimation model provided accurate tracking of distension pulse wave propagation in human carotid arteries in vivo (mean relative error 3.86 ± 2.69%).
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