  Doppler echocardiography offers critical insights into cardiac function and
phases by quantifying blood flow velocities and evaluating myocardial motion.
However, previous methods for automating Doppler analysis, ranging from initial
signal processing techniques to advanced deep learning approaches, have been
constrained by their reliance on electrocardiogram (ECG) data and their
inability to process Doppler views collectively. We introduce a novel unified
framework using a convolutional neural network for comprehensive analysis of
spectral and tissue Doppler echocardiography images that combines automatic
measurements and end-diastole (ED) detection into a singular method. The
network automatically recognizes key features across various Doppler views,
with novel Doppler shape embedding and anti-aliasing modules enhancing
interpretation and ensuring consistent analysis. Empirical results indicate a
consistent outperformance in performance metrics, including dice similarity
coefficients (DSC) and intersection over union (IoU). The proposed framework
demonstrates strong agreement with clinicians in Doppler automatic measurements
and competitive performance in ED detection.
