[论文解读] 4D-UNet improves clutter rejection in human transcranial contrast enhanced ultrasound

Tristan Beruard, Armand Delbos+3 more|arXiv (Cornell University)|Feb 20, 2026

Ultrasound and Hyperthermia Applications被引用 0

一句话总结

本工作提出了一个用于去除颅脑经皮3D CEUS中杂散信号的4D U-Net，利用时空信息在成人数据中增强微泡检测。

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.

研究动机与目标

• 解决颅脑经皮CEUS中低信噪比数据集的杂散信号问题。
• 开发利用时空信息以改善微泡检测的4D U-Net 架构。
• 在成年人体颅脑经皮CEUS数据上验证该方法并评估杂散信号滤除的改进。

提出的方法

• 设计扩展了3D CEUS处理以包含时间信息的4D-UNet架构。
• 在3D CEUS帧之间利用时空特征来抑制杂散信号。
• 训练网络在颅脑经数据中区分微泡信号与组织杂散信号。
• 将深度学习整合到CEUS处理以增强血管信号的可视化。

实验结果

研究问题

• RQ1 4D-UNet 是否能在与传统方法相比的情况下改善人类颅脑经皮CEUS中的杂散抑制？
• RQ2 在低SNR条件下，融入时间信息是否提升微泡可视化？
• RQ3 4D-UNet 如何影响CEUS可视化质量及对神经血管成像的潜在诊断价值？

主要发现

• 4D-UNet 提升了经颅CEUS中的时间域杂散滤波。
• 该方法在成人成人颅脑经皮数据中增强了微泡检测。
• 将深度学习整合到CEUS显示中显示出改善杂散抑制与可视化的潜力。
• 研究支持人工智能驱动的神经血管成像与临床诊断的改进。