  Color Doppler echocardiography enables visualization of blood flow within the
heart. However, the limited frame rate impedes the quantitative assessment of
blood velocity throughout the cardiac cycle, thereby compromising a
comprehensive analysis of ventricular filling. Concurrently, deep learning is
demonstrating promising outcomes in post-processing of echocardiographic data
for various applications. This work explores the use of deep learning models
for intracardiac Doppler velocity estimation from a reduced number of filtered
I/Q signals. We used a supervised learning approach by simulating patient-based
cardiac color Doppler acquisitions and proposed data augmentation strategies to
enlarge the training dataset. We implemented architectures based on
convolutional neural networks. In particular, we focused on comparing the U-Net
model and the recent ConvNeXt models, alongside assessing real-valued versus
complex-valued representations. We found that both models outperformed the
state-of-the-art autocorrelator method, effectively mitigating aliasing and
noise. We did not observe significant differences between the use of real and
complex data. Finally, we validated the models on in vitro and in vivo
experiments. All models produced quantitatively comparable results to the
baseline and were more robust to noise. ConvNeXt emerged as the sole model to
achieve high-quality results on in vivo aliased samples. These results
demonstrate the interest of supervised deep learning methods for Doppler
velocity estimation from a reduced number of acquisitions.
