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.

通过将 Eulerian Video Magnification 技术与 VibNet 框架结合，利用外部步进电机引起针的微弱振动，本论文提出了一种增强超声图像中针检测的鲁棒性和准确性的学习框架，实现了超声引导下针的鲁棒和精确检测。