In clinical applications that involve ultrasound-guided intervention, the visibility of the needle can be severely impeded due to steep insertion and strong distractors such as speckle noise and anatomical occlusion. To address this challenge, we propose VibNet, a learning-based framework tailored to enhance the robustness and accuracy of needle detection in ultrasound images, even when the target becomes invisible to the naked eye. Inspired by Eulerian Video Magnification techniques, we utilize an external step motor to induce low-amplitude periodic motion on the needle. These subtle vibrations offer the potential to generate robust frequency features for detecting the motion patterns around the needle. To robustly and precisely detect the needle leveraging these vibrations, VibNet integrates learning-based Short-Time-Fourier-Transform and Hough-Transform modules to achieve successive sub-goals, including motion feature extraction in the spatiotemporal space, frequency feature aggregation, and needle detection in the Hough space. Based on the results obtained on distinct ex vivo porcine and bovine tissue samples, the proposed algorithm exhibits superior detection performance with efficient computation and generalization capability.

翻译：在超声引导介入的临床应用中，针体的可视性常因陡峭插入角度及强干扰因素（如散斑噪声与解剖遮挡）而严重受限。针对该挑战，我们提出VibNet框架——一种基于学习的方法，旨在提升超声图像中针体检测的鲁棒性与精确度，即便目标在裸眼下完全不可见。受欧拉影像放大技术启发，我们利用外部步进电机在针体上诱发低幅周期运动。这些微小振动能够产生稳健的频率特征，用于检测针体周围的运动模式。为利用这些振动实现稳健精确的检测，VibNet集成了基于学习的短时傅里叶变换模块与霍夫变换模块，通过分步实现以下子目标：时空域运动特征提取、频率特征聚合，以及霍夫空间中的针体检测。基于离体猪与牛组织样本的实验结果表明，该算法在保持高效计算能力与泛化性能的同时，展现出优越的检测性能。