Deep needle insertion to a target often poses a huge challenge, requiring a
combination of specialized skills, assistive technology, and extensive
training. One of the frequently encountered medical scenarios demanding such
expertise includes the needle insertion into a femoral vessel in the groin.
After the access to the femoral vessel, various medical procedures, such as
cardiac catheterization and extracorporeal membrane oxygenation (ECMO) can be
performed. However, even with the aid of Ultrasound imaging, achieving
successful insertion can necessitate multiple attempts due to the complexities
of anatomy and tissue deformation. To address this challenge, this paper
presents an innovative technology for needle tip real-time tracking, aiming for
enhanced needle insertion guidance. Specifically, our approach revolves around
the creation of scattering imaging using an optical fiber-equipped needle, and
uses Convolutional Neural Network (CNN) based algorithms to enable real-time
estimation of the needle tip's position and orientation during insertion
procedures. The efficacy of the proposed technology was rigorously evaluated
through three experiments. The first two experiments involved rubber and bacon
phantoms to simulate groin anatomy. The positional errors averaging 2.3+1.5mm
and 2.0+1.2mm, and the orientation errors averaging 0.2+0.11rad and
0.16+0.1rad. Furthermore, the system's capabilities were validated through
experiments conducted on fresh porcine phantom mimicking more complex
anatomical structures, yielding positional accuracy results of 3.2+3.1mm and
orientational accuracy of 0.19+0.1rad. Given the average femoral arterial
radius of 4 to 5mm, the proposed system is demonstrated with a great potential
for precise needle guidance in femoral artery insertion procedures. In
addition, the findings highlight the broader potential applications of the
system in the medical field.

本研究提出了一种用于增强针尖插入指导的创新技术，基于散射成像和卷积神经网络，实现了针尖位置和方向的实时估计，通过实验证实了在股动脉插管过程中准确引导针头的潜力。