4D-UNet提升人类颅骨超声成像的杂波抑制能力
4D-UNet improves clutter rejection in human transcranial contrast enhanced ultrasound
AI总结：
4D-UNet通过整合深度学习提升颅骨超声成像中微气泡的检测与杂波抑制能力。
摘要：
颅骨超声成像受到高颅骨吸收率的限制，使得血管成像只能限于最大的血管。传统杂波滤波器在低信噪比（SNR）的超声数据集中表现不佳，其中血液和组织信号难以轻易分离，即使使用对比剂增强血液的回声特性。本文提出了一种新颖的4D U-Net方法，用于处理颅骨3D对比增强超声（CEUS）中的杂波过滤，通过4D-UNet实现来增强成人人体颅骨数据中的微气泡检测。我们的结果表明，4D-UNet能够提升时间杂波滤波器。通过将深度学习整合到CEUS中，本研究推进了神经血管成像，提供了改进的杂波抑制和可视化。研究结果强调了AI驱动方法在增强超声医学成像中的潜力，为更准确的诊断和更广泛的应用铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
