Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic
  Images Using a 3D Convolutional Auto-Encoder
Mahdi Tabassian Somayeh Akbari. S Sandro Queirós Jan D'hooge
This study presents a deep convolutional auto-encoder network for filtering
reverberation artifacts, from transthoracic echocardiographic (TTE) image
sequences. Given the spatiotemporal nature of these artifacts, the filtering
network was built using 3D convolutional layers to suppress the clutter
patterns throughout the cardiac cycle. The network was designed by taking
advantage of: i) an attention mechanism to focus primarily on cluttered regions
and ii) residual learning to preserve fine structures of the image frames. To
train the deep network, a diverse set of artifact patterns was simulated and
the simulated patterns were superimposed onto artifact-free ultra-realistic
synthetic TTE sequences of six ultrasound vendors to generate input of the
filtering network. The artifact-free sequences served as ground-truth.
Performance of the filtering network was evaluated using unseen synthetic as
well as in-vivo artifactual sequences. Satisfactory results obtained using the
latter dataset confirmed the good generalization performance of the proposed
network which was trained using the synthetic sequences and simulated artifact
patterns. Suitability of the clutter-filtered sequences for further processing
was assessed by computing segmental strain curves from them. The results showed
that the large discrepancy between the strain profiles computed from the
cluttered segments and their corresponding segments in the clutter-free images
was significantly reduced after filtering the sequences using the proposed
network. The trained deep network could process an artifactual TTE sequence in
a fraction of a second and can be used for real-time clutter filtering.
Moreover, it can improve the precision of the clinical indexes that are
computed from the TTE sequences. The source code of the proposed method is
available at:
https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main.