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.

翻译：深部靶点针穿刺通常面临巨大挑战，需要结合专业技能、辅助技术及大量训练。此类技术要求常见于腹股沟区股血管穿刺等医疗场景。在完成股血管穿刺后，可实施心导管术、体外膜肺氧合（ECMO）等多种医疗操作。然而，即使借助超声成像引导，由于解剖结构复杂性和组织变形等因素，成功穿刺仍可能需要多次尝试。针对这一挑战，本文提出了一种用于针尖实时追踪的创新技术，旨在提升穿刺引导精度。具体而言，我们的方法基于光纤针具产生散射成像，并采用基于卷积神经网络（CNN）的算法，在穿刺过程中实时估算针尖位置与方向。通过三项实验严格验证了所提技术的有效性：前两项实验采用橡胶和培根仿体模拟腹股沟解剖结构，位置误差平均值分别为2.3±1.5mm和2.0±1.2mm，方向误差平均值分别为0.2±0.11rad和0.16±0.1rad；随后在模拟更复杂解剖结构的新鲜猪仿体实验中验证了系统性能，位置精度达3.2±3.1mm，方向精度达0.19±0.1rad。鉴于股动脉平均半径为4-5mm，本系统在股动脉穿刺引导中展现出巨大潜力。此外，研究结果表明该系统在医学领域具有更广泛的应用前景。