■随着超声引导程序的快速发展,对于人工智能和超声引导的设备测试,对于具有足够解剖细节的回声体模存在未满足的需求。我们开发了一种用于创建新型耳鼻喉科相关设备测试的颈部体模的方法。为了实现解剖结构的准确表示,我们利用CT扫描和3D打印技术来创建定制的琼脂模具,从而提供高保真但具有成本效益的工具。
■根据以前的研究,我们颈部幻影的关键部件包括颈椎,气管,颈总动脉,颈内静脉,甲状腺,和周围的软组织。使用开源图像分析软件来处理CT数据以生成目标结构的高保真3D模型。树脂模具被3D打印并用各种琼脂混合物填充以模拟解剖学回声性。
■按照所提出的方法,我们成功地组装了颈部体模,它提供了目标结构的详细表示。为了评估结果,收集体模和活体组织的超声数据,并用ImageJ进行分析。我们能够证明与活组织相当的回声性。
■所提出的构建具有详细解剖特征的颈部体模的方法提供了有价值的,detailed,用于耳鼻喉科医疗培训和设备测试的低成本工具,特别是对于涉及人工智能(AI)引导和基于机器人的针头插入的新型设备。额外的解剖学改进和验证研究可以进一步提高一致性和准确性,从而为超声训练和研究的未来发展铺平了道路,最终有利于患者的护理和安全。
UNASSIGNED: With rapid advances in ultrasound-guided procedures, there is an unmet need for echogenic phantoms with sufficient anatomical details for artificial intelligence and ultrasound-guided device testing. We developed a method for creating neck phantoms for novel otolaryngology-related device testing. To achieve accurate representation of the anatomy, we utilized CT scans and 3D printing technology to create customized
agar molds, thus providing high-fidelity yet cost-effective tools.
UNASSIGNED: Based on previous studies, the key components in our neck phantom include the cervical vertebrae, trachea, common carotid arteries, internal jugular veins, thyroid gland, and surrounding soft tissue. Open-source image analysis software were employed to process CT data to generate high fidelity 3D models of the target structures. Resin molds were 3D printed and filled with various
agar mixtures to mimic anatomical echogenicity.
UNASSIGNED: Following the method proposed, we successfully assembled the neck phantom which provided a detailed representation of the target structures. To evaluate the results, ultrasound data was collected on the phantom and living tissue and analyzed with ImageJ. We were able to demonstrate echogenicity comparable to that of living tissue.
UNASSIGNED: The proposed method for building neck phantoms with detailed anatomical features offers a valuable, detailed, low-cost tool for medical training and device testing in otolaryngology, particularly for novel devices that involve artificial intelligence (AI) guidance and robotic-based needle insertion. Additional anatomical refinements and validation studies could further enhance the consistency and accuracy, thus paving the way for future advancements in ultrasound training and research, and ultimately benefiting patient care and safety.