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arXiv · 2602.18542

4D-UNet improves clutter rejection in human transcranial contrast enhanced ultrasound

Tristan Beruard·Armand Delbos·Arthur Chavignon·Maxence Reberol·Vincent Hingot
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Abstract

Transcranial ultrasound imaging is limited by high skull absorption, limiting vascular imaging to only the largest vessels. Traditional clutter filters struggle with low signal-to-noise ratio (SNR) ultrasound datasets, where blood and tissue signals cannot be easily separated, even when the echogenicity of the blood is improved with contrast agents. Here, we present a novel 4D U-Net approach for clutter filtering in transcranial 3D Contrast Enhanced Ultrasound (CEUS) exploiting spatial and temporal information via a 4D-UNet implementation to enhance microbubble detection in transcranial data acquired in human adults. Our results show that the 4D-UNet improves temporal clutter filters. By integrating deep learning into CEUS, this study advances neurovascular imaging, offering improved clutter rejection and visualization. The findings underscore the potential of AI-driven approaches to enhance ultrasound-based medical imaging, paving the way for more accurate diagnostics and broader clinical applications.

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Tristan Beruard, Armand Delbos, Arthur Chavignon, Maxence Reberol, Vincent Hingot. 2026-02-20. 4D-UNet improves clutter rejection in human transcranial contrast enhanced ultrasound. https://arxiv.org/abs/2602.18542

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