用4D-UNet提升颅脑超声中微泡检测，改善噪声干扰
4D-UNet improves clutter rejection in human transcranial contrast enhanced ultrasound
- 结合时空信息的4D-UNet深度学习模型，优化血流与组织信号分离
- 在人体成人数据上显著提升微泡检测能力，增强血管成像清晰度
- 适合超声影像、神经科学及临床诊断领域，推动AI辅助医学成像
经颅超声成像受限于颅骨高吸收率，导致血管成像仅能覆盖最大血管。传统杂波滤波器在低信噪比（SNR）超声数据中难以区分血流与组织信号，即使使用对比剂提高血流回声强度亦然。本文提出一种新型4D-UNet方法，利用4D-UNet架构在经颅3D对比增强超声（CEUS）中融合空间与时间信息，以提升微泡检测性能。实验结果表明，该方法有效改进了时序杂波滤波效果。通过将深度学习融入CEUS，本研究推动了神经血管成像发展，实现更优杂波抑制与可视化。研究凸显了人工智能驱动方法在超声医学成像中的潜力，为更精准诊疗和广泛临床应用铺平道路。
原文摘要 · Abstract (English)
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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