  Color Doppler echocardiography is a widely used non-invasive imaging modality
that provides real-time information about the intracardiac blood flow. In an
apical long-axis view of the left ventricle, color Doppler is subject to phase
wrapping, or aliasing, especially during cardiac filling and ejection. When
setting up quantitative methods based on color Doppler, it is necessary to
correct this wrapping artifact. We developed an unfolded primal-dual network to
unwrap (dealias) color Doppler echocardiographic images and compared its
effectiveness against two state-of-the-art segmentation approaches based on
nnU-Net and transformer models. We trained and evaluated the performance of
each method on an in-house dataset and found that the nnU-Net-based method
provided the best dealiased results, followed by the primal-dual approach and
the transformer-based technique. Noteworthy, the primal-dual network, which had
significantly fewer trainable parameters, performed competitively with respect
to the other two methods, demonstrating the high potential of deep unfolding
methods. Our results suggest that deep learning-based methods can effectively
remove aliasing artifacts in color Doppler echocardiographic images,
outperforming DeAN, a state-of-the-art semi-automatic technique. Overall, our
results show that deep learning-based methods have the potential to effectively
preprocess color Doppler images for downstream quantitative analysis.
