Deep Spatiotemporal Clutter Filtering (3D Convolutional Autoencoder)

3D convolutional autoencoder that filters reverberation clutter artifacts from transthoracic echocardiography (TTE) video sequences, improving downstream measurements such as speckle-tracking strain. Built on a 3D U-Net-style encoder-decoder with an input-output skip connection to preserve fine structures and attention-gate modules to focus on cluttered regions, the network was trained on synthetic clutter simulated across six ultrasound vendors and generalized well to real in vivo artifactual sequences, substantially reducing the discrepancy between cluttered and clutter-free strain profiles while running in a fraction of a second per sequence.