Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images: Leveraging Contextual Attention and Residual Learning
This work addresses the challenge of improving precision in clinically relevant indices derived from echocardiographic images for medical diagnostics, though it is incremental as it builds on existing deep learning methods with specific enhancements.
This study tackled the problem of filtering reverberation clutter from transthoracic echocardiographic image sequences using a deep convolutional autoencoder network, resulting in a significant reduction in discrepancy between strain profiles from cluttered and clutter-free segments and enabling real-time processing in a fraction of a second.
This study presents a deep convolutional autoencoder network for filtering reverberation clutter from transthoracic echocardiographic (TTE) image sequences. Given the spatiotemporal nature of this type of clutter, the filtering network employs 3D convolutional layers to suppress it throughout the cardiac cycle. The design of the network incorporates two key features that contribute to the effectiveness of the filter: 1) an attention mechanism for focusing on cluttered regions and leveraging contextual information, and 2) residual learning for preserving fine image structures. To train the network, a diverse set of artifact patterns was simulated and superimposed onto ultra-realistic synthetic TTE sequences from six ultrasound vendors, generating input for the filtering network. The artifact-free sequences served as ground-truth. Performance of the filtering network was evaluated using unseen synthetic and in vivo artifactual sequences. Results from the in vivo dataset confirmed the network's strong generalization capabilities, despite being trained solely on synthetic data and simulated artifacts. The suitability of the filtered sequences for downstream processing was assessed by computing segmental strain curves. A significant reduction in the discrepancy between strain profiles computed from cluttered and clutter-free segments was observed after filtering the cluttered sequences with the proposed network. The trained network processes a TTE sequence in a fraction of a second, enabling real-time clutter filtering and potentially improving the precision of clinically relevant indices derived from TTE sequences. The source code of the proposed method and example video files of the filtering results are available at: \href{https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main}{https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main}.