Vascular wall motion detection models based on long short-term memory in plane-wave-based ultrasound imaging

Objective. Vascular wall motion can be used to diagnose cardiovascular diseases. In this study, long short-term memory (LSTM) neural networks were used to track vascular wall motion in plane-wave-based ultrasound imaging. Approach. The proposed LSTM and convolutional LSTM (ConvLSTM) models were trained using ultrasound data from simulations and tested experimentally using a tissue-mimicking vascular phantom and an in vivo study using a carotid artery. The performance of the models in the simulation was evaluated using the mean square error from axial and lateral motions and compared with the cross-correlation (XCorr) method. Statistical analysis was performed using the Bland–Altman plot, Pearson correlation coefficient, and linear regression in comparison with the manually annotated ground truth. Main results. For the in vivo data, the median error and 95% limit of agreement from the Bland–Altman analysis were (0.01, 0.13), (0.02, 0.19), and (0.03, 0.18), the Pearson correlation coefficients were 0.97, 0.94, and 0.94, respectively, and the linear equations were 0.89 x + 0.02, 0.84 x + 0.03, and 0.88 x + 0.03 from linear regression for the ConvLSTM model, LSTM model, and XCorr method, respectively. In the longitudinal and transverse views of the carotid artery, the LSTM-based models outperformed the XCorr method. Overall, the ConvLSTM model was superior to the LSTM model and XCorr method. Significance. This study demonstrated that vascular wall motion can be tracked accurately and precisely using plane-wave-based ultrasound imaging and the proposed LSTM-based models.