Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US image presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker MambaXCTrack utilizing structured state space models cross-correlation (SSMX-Corr) and implicit motion prompt is proposed, which is the first application of Mamba in US needle tracking. The SSMX-Corr enhances cross-correlation by long-range modeling and global searching of distant semantic features between template and search maps, benefiting the tracking under noise and artifacts by implicitly learning potential distant semantic cues. By combining with cross-map interleaved scan (CIS), local pixel-wise interaction with positional inductive bias can also be introduced to SSMX-Corr. The implicit low-level motion descriptor is proposed as a non-visual prompt to enhance tracking robustness, addressing the intermittent tip visibility problem. Extensive experiments on a dataset with motorized needle insertion in both phantom and tissue samples demonstrate that the proposed tracker outperforms other state-of-the-art trackers while ablation studies further highlight the effectiveness of each proposed tracking module.

翻译：超声引导下的针头插入在经皮介入手术中广泛应用。然而，由于噪声、伪影以及超声成像平面较薄，通过超声图像提供针尖位置反馈面临挑战，这些因素会削弱针的特征并导致针尖可见性间歇性中断。本文提出了一种基于Mamba的超声针跟踪器MambaXCTrack，它利用结构化状态空间模型互相关（SSMX-Corr）和隐式运动提示，这是Mamba在超声针跟踪中的首次应用。SSMX-Corr通过长程建模和全局搜索模板图与搜索图之间的远距离语义特征来增强互相关，通过隐式学习潜在的远距离语义线索，有助于在噪声和伪影下的跟踪。结合跨图交错扫描（CIS），SSMX-Corr还能引入具有位置归纳偏置的局部像素级交互。提出的隐式低级运动描述符作为一种非视觉提示，增强了跟踪的鲁棒性，解决了针尖可见性间歇中断的问题。在包含仿体和组织样本的电动针插入数据集上进行的大量实验表明，所提出的跟踪器性能优于其他最先进的跟踪器，消融研究进一步验证了每个所提跟踪模块的有效性。