Ultrasound (US) imaging is widely used in diagnosing and staging abdominal diseases due to its lack of non-ionizing radiation and prevalent availability. However, significant inter-operator variability and inconsistent image acquisition hinder the widespread adoption of extensive screening programs. Robotic ultrasound systems have emerged as a promising solution, offering standardized acquisition protocols and the possibility of automated acquisition. Additionally, these systems enable access to 3D data via robotic tracking, enhancing volumetric reconstruction for improved ultrasound interpretation and precise disease diagnosis. However, the interpretability of 3D US reconstruction of abdominal images can be affected by the patient's breathing motion. This study introduces a method to compensate for breathing motion in 3D US compounding by leveraging implicit neural representations. Our approach employs a robotic ultrasound system for automated screenings. To demonstrate the method's effectiveness, we evaluate our proposed method for the diagnosis and monitoring of abdominal aorta aneurysms as a representative use case. Our experiments demonstrate that our proposed pipeline facilitates robust automated robotic acquisition, mitigating artifacts from breathing motion, and yields smoother 3D reconstructions for enhanced screening and medical diagnosis.

翻译：超声成像因其无电离辐射及广泛的可及性，在腹部疾病的诊断与分期中具有重要应用价值。然而，操作者间显著的操作差异性和图像采集的不一致性制约了大范围筛查项目的推广应用。机器人超声系统作为有前景的解决方案应运而生，其具备标准化采集协议与自动化采集能力。此外，这类系统通过机器人追踪可获取三维数据，增强体素重建能力以提升超声判读精度和疾病诊断准确性。但腹部图像的三维超声重建结果易受患者呼吸运动影响。本研究提出一种利用隐式神经表征补偿三维超声复合中呼吸运动的方法。该方法采用机器人超声系统实现自动化筛查。为验证有效性，我们以腹主动脉瘤的诊断与监测为典型应用案例进行评估。实验表明：本文提出的流程可支持稳健的自动化机器人采集，有效抑制呼吸运动伪影，并获得更平滑的三维重建结果，为增强型筛查与医学诊断提供技术支持。