利用模拟肝脏模型和 ICP 跟踪技术开发活体肝脏移植 MR 培训系统

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Human-Machine Systems Pub Date : 2024-09-10 DOI:10.1109/THMS.2024.3450689
Tsung-Han Yang;Yi-Chun Du;Cheng-Bin Xu;Wei-Siang Ciou
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引用次数: 0

摘要

活体肝移植(LT)是治疗失代偿性肝硬化、某些代谢性疾病和急性肝功能衰竭的一种治愈性疗法。对于肝细胞癌的特殊情况,活体肝移植比其他已知治疗方法的预后更好。在活体肝移植过程中,识别和保留肝中静脉(MHV)及其主要分支极为重要,与供体和受体的预后密切相关。目前,术前计算机断层扫描(CT)和术中超声检查被用于评估肝中静脉的位置;然而,CT 扫描和超声检查的信息是二维的,缺乏具体的感知数据。为了在手术过程中更好地追踪 MHV,这项研究提出了一种用于开放式肝脏 LT 手术的混合现实(MR)训练系统,该系统采用了模拟弹性肝脏模型和迭代最邻近点(ICP)追踪技术。我们根据 20 名患者的 CT 图像创建了一个三维(3-D)肝脏重建模型,并制作了一系列大小相等的弹性肝脏模型,模型内部有柔软的血管。使用 ICP 算法用磁共振系统跟踪肝脏模型,并将三维重建模型叠加到模型上。实验结果表明,配准误差小于 4 毫米。十名新手外科医生使用该系统进行了练习,反馈良好。预计拟议的 LT 系统可提高外科医生培训的整体效果,并为今后的其他应用提供参考。
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Development of a MR Training System for Living Donor Liver Transplantation Using Simulated Liver Phantom and ICP Tracking Technology
Living donor liver transplantation (LT) is a curative treatment for decompensation liver cirrhosis, some metabolic diseases, and acute liver failure. For specific conditions of hepatocellular carcinoma, LT provides a better prognosis than other known treatments do. During living donor LT, recognition and preservation of the middle hepatic vein (MHV) and its main branch are extremely important and closely related to the outcomes for the donor and recipient. Currently, preoperative computed tomography (CT) scans and intraoperative ultrasound are used to evaluate the location of the MHV; however, the information from CT scans and ultrasound is two-dimensional and lacks specific perception data. To achieve better MHV tracking during surgery, this work presents a mixed-reality (MR) training system for open liver LT surgery, which uses a simulated elastic liver phantom and iterative closest point (ICP) tracking technology. We created a three-dimensional (3-D) liver reconstruction model based on CT images from 20 patients and produced a series of equal-sized elastic liver phantoms with soft vessels inside. The ICP algorithm was used to track the liver phantom with the MR system, and the 3-D reconstruction model was superimposed on the phantom. The experimental results revealed that the registration error was <4 mm. The feedback from ten novice surgeons who practiced with the proposed system was positive. It is expected that the proposed system for LT could enhance the overall effectiveness of surgeon training and serve as a reference for other applications in the future.
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IEEE Transactions on Human-Machine Systems
IEEE Transactions on Human-Machine Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
7.10
自引率
11.10%
发文量
136
期刊介绍: The scope of the IEEE Transactions on Human-Machine Systems includes the fields of human machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.
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Table of Contents Call for Papers: IEEE Transactions on Human-Machine Systems IEEE Transactions on Human-Machine Systems Information for Authors IEEE Systems, Man, and Cybernetics Society Information IEEE Systems, Man, and Cybernetics Society Information
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