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和广泛可及性，常用于腹部疾病的诊断与分期。然而，操作者间显著差异和不一致的图像采集限制了大规模筛查的推广。机器人超声系统作为一种有前景的解决方案，提供了标准化采集协议及自动化采集的可能性。此外，这类系统通过机器人追踪获取三维数据，增强了体素重建能力，从而改善超声图像判读和精确疾病诊断。然而，腹部图像的三维超声重建可解释性可能受患者呼吸运动影响。本研究提出一种方法，通过利用隐式神经表示补偿三维超声复合中的呼吸运动。我们的方法采用机器人超声系统进行自动化筛查。为展示该方法的有效性，我们以腹主动脉瘤的诊断与监测作为代表性用例进行评估。实验表明，我们提出的流程促进了稳健的自动化机器人采集，减轻了呼吸运动伪影，并生成更平滑的三维重建，从而增强筛查与医学诊断效能。