Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images: Leveraging Contextual Attention and Residual Learning

2024-01-23

AI Summary

Speckle and reverberation artifacts in transthoracic echocardiography (TTE) sequences severely degrade image quality and impede quantitative analysis, particularly myocardial strain estimation. Method: We propose a lightweight 3D convolutional autoencoder incorporating channel-spatial joint attention and residual learning—marking the first integration of these mechanisms for end-to-end real-time TTE denoising. Crucially, the model is trained exclusively on synthetic data generated via multi-vendor ultrasound physics simulation, eliminating reliance on paired clean clinical data. Contribution/Results: The framework achieves sub-second inference time per sequence (<1 s), satisfying real-time clinical requirements. Quantitative evaluation demonstrates significant reduction in strain curve error and marked improvement in downstream quantitative metrics (e.g., peak systolic strain accuracy). Importantly, it exhibits strong generalization to unseen real-world TTE sequences across vendors. This work establishes a clinically deployable paradigm for ultrasound artifact suppression without ground-truth clean acquisitions.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Segmentation
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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}.

Problem

• Filtering reverberation clutter in echocardiographic images
• Using 3D convolutional layers for spatiotemporal suppression
• Enhancing image clarity with attention and residual learning

Innovation

• 3D convolutional layers
• contextual attention mechanism
• residual learning preservation