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This study presents a deep convolutional auto-encoder network for filteringreverberation artifacts, from transthoracic echocardiographic (TTE) imagesequences. Given the spatiotemporal nature of these artifacts, the filteringnetwork was built using 3D convolutional layers to suppress the clutterpatterns throughout the cardiac cycle. The network was designed by takingadvantage of: i) an attention mechanism to focus primarily on cluttered regionsand ii) residual learning to preserve fine structures of the image frames. Totrain the deep network, a diverse set of artifact patterns was simulated andthe simulated patterns were superimposed onto artifact-free ultra-realisticsynthetic TTE sequences of six ultrasound vendors to generate input of thefiltering network. The artifact-free sequences served as ground-truth.Performance of the filtering network was evaluated using unseen synthetic aswell as in-vivo artifactual sequences. Satisfactory results obtained using thelatter dataset confirmed the good generalization performance of the proposednetwork which was trained using the synthetic sequences and simulated artifactpatterns. Suitability of the clutter-filtered sequences for further processingwas assessed by computing segmental strain curves from them. The results showedthat the large discrepancy between the strain profiles computed from thecluttered segments and their corresponding segments in the clutter-free imageswas significantly reduced after filtering the sequences using the proposednetwork. The trained deep network could process an artifactual TTE sequence ina fraction of a second and can be used for real-time clutter filtering.Moreover, it can improve the precision of the clinical indexes that arecomputed from the TTE sequences. The source code of the proposed method isavailable at:https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main.

Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images Using a 3D Convolutional Auto-Encoder